Vegetation net primary productivity space-time trend analysis and climate and human contribution analysis method

Abstract

The invention discloses a vegetation net primary productivity space-time trend analysis and climate and human contribution analysis method. The method comprises the following steps: (1) carrying out space-time trend analysis of different space unit vegetation NPP; (2) dividing the vegetation net primary productivity NPP into three types, namely actual vegetation net primary productivity ANPP, potential vegetation net primary productivity PNPP and artificial vegetation net primary productivity HNPP, and calculating to obtain three types of NPP data; and (3) according to the three types of NPP data obtained in the step (2), climate and human contribution analysis of different space unit vegetation NPP is carried out. The method has the advantages that time-space trend analysis of the vegetation net primary productivity is carried out, contribution characteristics of climate changes and human activities are quantified, and a solid foundation is laid for deeply understanding change characteristics and contribution rules of ecological system productivity and promoting implementation of a dual-carbon strategy and sustainable development.

Description

Technical Field

[0001] The present invention relates to a method for analyzing the spatiotemporal trend of vegetation net primary productivity and analyzing climate and human contribution, and belongs to the technical field of geographic information system development. Background Art

[0002] Due to complex interactions, the changing characteristics of regional carbon budgets are highly complex and variable, ultimately resulting in significant gaps in the analysis of the contributions of climate change and human activities. For example, in some ecologically fragile areas, climate change may lead to reduced precipitation and increased temperatures, which may further aggravate environmental problems and cause a significant decline in vegetation net primary productivity (NPP). Human activities such as overgrazing and unreasonable development may also exacerbate this trend. The interactions between these influencing factors are complex and difficult to quantify, so accurately separating their contributions to changes in carbon budgets is a huge challenge.

[0003] The residual analysis method is simple to calculate and has clear biological significance. It is a potential method to separate climate and human contributions and has been successfully applied in different regions. For example, in the prior art, some scholars have explained the contribution of climate (42.35%) and human (57.65%) to NPP in the Loess Plateau (Zheng K, Wei JZ, Pei JY, et al. Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Science of the Total Environment, 2019, 660: 236-244). Some scholars have also used a combination of residual analysis, terrestrial ecosystem model (TEM) and CASA simulation to analyze the impact of climate and human factors on NPP in the Qinghai-Tibet Plateau (Chen BX, Zhang XZ, Tao J, et al. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 2014, 189: 11-18). In addition, some scholars have concluded through residual analysis that the contributions of humans and climate to NPP in China are 60.06% and 39.94%, respectively (Ge WY, Deng LQ, Wang F, et al. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Science of the Total Environment, 2021, 773). However, most of the above studies focus on the regional level, and their specific spatial details are still unclear. Therefore, it is urgent to conduct a comprehensive analysis of the driving factors at the spatial unit scale.

[0004] In view of the above situation, it is urgent to analyze the contribution of climate change and human activities in each spatial unit, which will help understand and grasp the intrinsic relationship between national land space development and regional carbon balance, and provide scientific information support for the development of differentiated national land space planning and dual carbon strategies in accordance with local conditions. Summary of the invention

[0005] In response to the above-mentioned technical problems existing in the prior art, the present invention provides a method for analyzing the spatiotemporal trends of net primary productivity of vegetation and analyzing climate and human contributions, thereby exploring the intrinsic relationship between national land space development and regional carbon balance.

[0006] In order to solve the above problems, the present invention adopts the following technical solutions:

[0007] A method for analyzing the spatiotemporal trend of vegetation net primary productivity and the climate and human contribution analysis includes the following steps:

[0008] Step (i) Conducting spatiotemporal trend analysis of vegetation NPP in different spatial units;

[0009] Step (ii) Divide the vegetation net primary productivity NPP into: actual vegetation net primary productivity A NPP , potential vegetation net primary productivity P NPP and the net primary productivity of anthropogenic vegetation H NPP Three categories, and calculate the three categories of NPP data;

[0010] Step (iii) analyzing the climate and human contribution of vegetation NPP in different spatial units based on the three types of NPP data obtained in step (ii).

[0011] As an optimization solution, step (1) includes the following sub-steps:

[0012] Sub-step 1-1: Data acquisition and preprocessing: Obtain vegetation net primary productivity (NPP) data and temperature and precipitation data in the study area from 2000 to 2022;

[0013] Sub-step 1-2: Combined with the division of national land space units, the linear trend analysis method is used to analyze the interannual trend of vegetation NPP in different spatial units, and the NPP slope K is used to represent the temporal trend of NPP;

[0014] Sub-step 1-3: Use a method combining the Hurst index and linear trend analysis to predict the future trend of net primary productivity of vegetation in different spatial units.

[0015] As an optimization solution, the data acquisition methods in step (ii) are:

[0016] The actual net primary productivity of vegetation A NPP Obtained from MODIS products;

[0017] The potential net primary productivity of vegetation P NPP represents the NPP affected only by climate change and is calculated as follows:

[0018] P NPP=3000[1-e -0.0009695(V-20) ]

[0019]

[0020] L=300+25T+0.05T 3

[0021] Wherein, V is evapotranspiration, in mm; R is precipitation, in mm; L is maximum evapotranspiration, in mm; T is temperature, in °C;

[0022] The net primary productivity of anthropogenic vegetation H NPP NPP caused by human activities, NPP With A NPP The difference between is calculated.

[0023] As an optimization solution, the step (iii) includes the following sub-steps:

[0024] Sub-step 3-1: Calculate the three types of NPP slopes K according to the three types of NPP data obtained in step (ii);

[0025] Sub-step 3-2: Use the three types of NPP slopes K to derive the impact of climate change and human activities on NPP: the degree of change in NPP is represented by ΔNPP;

[0026] Sub-step 3-3: Based on the change degree of NPP ΔNPP, the contribution rate of climate change and human activities to NPP is obtained.

[0027] Furthermore, the method of using the three types of NPP slopes K in step (iii) to evaluate the impact of climate change and human activities on NPP is:

[0028] ΔNPP=(n-1)×K

[0029] Where n is the total number of years, K is the slope of the NPP time series, and ΔNPP is the degree of change in NPP;

[0030] Furthermore, in the sub-step 3-3, according to A NPP Slope K a , P NPP Slope K p , H NPP Slope K h Identify 6 scenarios and NPP Change ΔNPP h and H NPP Change ΔNPP p The contribution rates of climate change and human activities under six scenarios were calculated respectively.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention conducts a linear trend analysis on the net primary productivity (NPP) of vegetation in different spatial units to reveal its interannual variation trend, and on this basis, combines the Hurst index to further predict the future trend of NPP in different spatial units; then, an effective method is proposed to quantify the impact of climate change and human activities on NPP, and six different scenarios are set to comprehensively evaluate the contribution of climate change and human activities to NPP changes in different spatial units. In summary, the present invention not only provides an in-depth understanding of the dynamic changes in vegetation net primary productivity, but also provides a scientific basis for formulating effective ecological protection and resource management strategies by quantifying the relative impact of climate and human activities.

[0033] This invention further conducts contribution analysis based on the linear trend analysis of vegetation net primary productivity (NPP) in different spatial units, which helps to deeply understand the specific role and contribution differences of each spatial unit in the carbon cycle, and provides a solid scientific basis for formulating differentiated carbon emission reduction strategies, optimizing national space layout, and promoting regional sustainable development. This measure can promote the formation of a more scientific and reasonable pattern of national space development and protection, ensure that while ensuring economic and social development, it effectively responds to climate change challenges and achieves a win-win situation for ecological environmental protection and economic and social development.

[0034] In summary, in response to the current lack of NPP contribution analysis technology, this invention carried out spatiotemporal trend analysis of vegetation net primary productivity and quantified the contribution characteristics of climate change and human activities, laying a solid foundation for a deep understanding of the changing characteristics and contribution laws of ecosystem productivity and promoting the implementation of the dual carbon strategy and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention;

[0036] Figure 2 The present invention provides an overall flow chart of the method. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below with reference to the accompanying drawings and examples.

[0038] The present invention proposes a method for dividing the territorial space units and analyzing their contributions for coupling regional carbon budget and main functional areas, and explores the territorial space unit division strategy for coupling regional carbon budget and main functional areas based on the GIS spatial analysis method.

[0039] The present invention combines the results of the division of national land space units with the net primary productivity of vegetation by using methods such as linear trend analysis and Hurst index to analyze the changing trends of different spatial units, and uses the Thornthwaite Memorial method combined with residual analysis to quantify the contribution of climate change and human activities to the change of net primary productivity of vegetation in different spatial units, which helps to improve the scientificity and rationality of national land space planning and provide strong support for promoting the construction of ecological civilization in my country and achieving high-quality development. The specific implementation methods of the method proposed in the present invention are described in detail below.

[0040] Step 1: Spatiotemporal trend analysis of vegetation NPP in different spatial units

[0041] Sub-step 1-1: Data acquisition and preprocessing: Obtain vegetation net primary productivity (NPP) data and temperature and precipitation data in the study area from 2000 to 2022; for the convenience of statistics and analysis, convert all data into Albers projection, and use the resampling method to unify the data to 1 km and 1 year.

[0042] Sub-step 1-2: Combined with the division of national land space units, the linear trend analysis method is used to analyze the interannual trend of vegetation NPP in different space units, and the NPP slope K is used to characterize the temporal variation trend of NPP; the linear trend analysis calculation method proposed in the present invention is as follows:

[0043] C t (i,j)=K(i,j)×t+b(i,j)

[0044] Where: t represents the year (2000 is the first year), (i, j) represents the spatial unit position, C t is the annual average NPP value of the study area in year t, K and b represent the coefficients of the fitting equation, and the slope K represents the temporal variation trend of NPP.

[0045] The calculation principle of slope K is to fit a straight line to a set of data points so that the sum of the squares of the vertical distances (i.e., residuals) from all data points to the straight line is minimized. Given a set of data points (i, NPP i ), where i is the index of time, NPP i is the NPP value at time i, n is the total number of years, and the slope K can be calculated by the following formula:

[0046]

[0047] The first term in the numerator first calculates the sum of the products of the i-th year and the corresponding NPP value and multiply it by the total number of years n; the second term in the numerator is the sum of the time The sum of the NPP values The product; the first term in the denominator part is the sum of the squares of time and multiplied by the total number of years n; the second term in the denominator part is the square of the sum of time By calculating these summation terms, the slope K can be obtained. When K > 0, it indicates that the NPP situation tends to develop in a positive trend; conversely, when K < 0, it indicates that the situation tends to decrease.

[0048] In addition, the significance value P of the linear trend of NPP is used to measure the reliability or significance of the trend of NPP over time. Specifically, it reflects whether the observed trend of NPP change is significantly different from random fluctuations statistically. If the P value is small (usually less than a preset significance level such as 0.05 or 0.01), it indicates that the change trend of NPP is significant, that is, this trend is unlikely to be caused by random factors. On the contrary, if the P value is large, it indicates that the observed change trend may be just random fluctuations and does not have statistical significance. Further, by evaluating the ranges of the slope K and the significance value P, the NPP trend is classified into the types shown in Table 1:

[0049] Table 1 Trend classification types

[0050] standard type K<0,P<0.01 Significantly reduced K<0,0.01<P<0.05 Significantly reduced P＞0.05 No significant changes K>0,0.01<P<0.05 Significant increase K>0,P<0.01 Significant increase

[0051] Sub-step 1-3: The Hurst exponent reflects the persistence of the time series data set. The present invention uses this characteristic to adopt a method combining the Hurst exponent and linear trend analysis to predict the future trend of vegetation net primary productivity in different spatial units.

[0052] Specifically, the Hurst exponent is between 0 and 1. 0 < H < 0.5 indicates that the future NPP is contrary to the past trend, H = 0.5 indicates that historical changes have a limited effect, and 0.5 < H < 1 indicates that the future NPP will follow historical changes. Combining the results of the national territorial space unit division in step 1, the present invention adopts a method combining the Hurst exponent and linear trend analysis to predict the future trend of NPP in different spatial units, and the classification is shown in Table 2:

[0053] Table 2 Types of future trends

[0054] Linear regression coefficient Hurst Index Types of future trends K>0 0.5<H<1 Continue to increase K>0 0<H<0.5 From increase to decrease K<0 0.5<H<1 Continued reduction K<0 0<H<0.5 From decrease to increase

[0055] Step (2) Divide the vegetation net primary productivity NPP into: actual vegetation net primary productivity A NPP , potential vegetation net primary productivity P NPP and anthropogenic vegetation net primary productivity H NPP into three categories, and calculate the three types of NPP data

[0056] Specifically, in order to clarify the impact of climate change and human activities on NPP, NPP is divided into actual NPP (A NPP ), potential NPP (P NPP ) and anthropogenic NPP (H NPP ) The methods of obtaining the three types of data are described in detail as follows:

[0057] (1) Actual vegetation net primary productivity A NPP Obtained from MODIS products;

[0058] (2) Potential vegetation net primary productivity P NPP Represents the NPP affected only by climate change. First, based on the average annual temperature T, the maximum evapotranspiration L is calculated. This formula takes into account the nonlinear effect of temperature on vegetation growth and captures this complexity through the cubic term. Next, the calculated maximum evapotranspiration L and annual precipitation R are substituted into the formula to calculate the evapotranspiration V. This formula takes into account the interaction between precipitation and maximum evapotranspiration, as well as their effects on actual evapotranspiration. Finally, the calculated evapotranspiration V is substituted into the formula to calculate the potential net primary productivity P of vegetation. NPP The formula is based on an exponential function, which reflects the effect of evapotranspiration V on potential vegetation net primary productivity P. NPP The nonlinear effect of V increases. NPP The specific calculation method is as follows:

[0059] L=300+25T+0.05T 3

[0060]

[0061] P NPP =3000[1-e -0.0009695(V-20) ]

[0062] Wherein, V is evapotranspiration, in mm; R is precipitation, in mm; L is maximum evapotranspiration, in mm; T is temperature, in °C;

[0063] (3) Net primary productivity of anthropogenic vegetation H NPP NPP caused by human activities, NPP With A NPP The difference between them is calculated as:

[0064] H NPP =P NPP -A NPP

[0065] H NPPA positive value indicates a decrease in NPP due to human activities, while a negative value indicates an increase in NPP.

[0066] Step (iii) Analyze the climate and human contribution of vegetation NPP in different spatial units based on the three types of NPP data obtained in step (ii)

[0067] Sub-step 3-1: Calculate the three types of NPP slopes K based on the three types of NPP data obtained in step (ii); that is, the actual NPP (A NPP ), potential NPP (P NPP ) and anthropogenic NPP (H NPP ), the linear trend analysis method provided in step (a) was used to calculate P NPP (K p ), A NPP (K a ) and H NPP (K h )

[0068] Sub-step 3-2: Use the three types of NPP slopes K to derive the impact of climate change and human activities on NPP (that is, use the slopes to evaluate the impact of climate change and human activities on NPP): The degree of change in NPP is expressed as ΔNPP, which is calculated as follows:

[0069] ΔNPP=(n-1)×K

[0070] Where n represents the total number of years and K represents the slope of the NPP time series.

[0071] Sub-step 3-3: Based on the change degree of NPP ΔNPP, the contribution rate of climate change and human activities to NPP is obtained.

[0072] Specifically, according to A NPP Slope K a , P NPP Slope K p , H NPP Slope K h Six scenarios were identified and the h and ΔNPP p The contribution rates of climate change and human activities under the six scenarios were calculated respectively. In other words, based on the above steps, six scenarios were determined to evaluate the contribution of climate and human activities to NPP. The specific evaluation methods are summarized as the specific analysis methods of climate and human activities to NPP defined in Table 3:

[0073] Table 3 Scenarios of climate and human contribution to NPP

[0074]

[0075] Among them, A NPP The slope is K a , P NPP The slope is K p , H NPP The slope is K h , ΔNPP p P NPP Change, ΔNPP h H NPP Amount of change.

[0076] In addition, the spatiotemporal trend analysis of vegetation NPP in different spatial units carried out in step (1) of the present invention may also include: firstly optimizing the spatial unit division. The present invention discloses the following spatial unit division optimization method, specifically a national land spatial unit division method coupling carbon budget and main functional area:

[0077] Step 1: Data acquisition and preprocessing The carbon emission, carbon absorption, GDP data, main functional zoning and vegetation net primary productivity (NPP) data of each district and county in the study area from 2000 to 2022 were obtained. In the case of missing carbon absorption data in some districts and counties, the data were supplemented by finding the mean of the adjacent districts and counties. To ensure the timeliness of the data, MODIS NPP data were collected at the same time, and the carbon absorption data were linearly fitted with the NPP data, and then the carbon absorption data were extended to 2022. Carbon emission data include carbon dioxide emissions from fossil and biofuels. The two are added as the total emissions, and then the regional summary statistics are obtained to obtain the carbon emission data of the districts and counties in the study area from 2000 to 2022. The carbon emission, carbon absorption and GDP data of each district and county in the study area in different years were sorted in different Excel tables, visualized by ArcGIS, and further analyzed by data connection.

[0078] Step 2: Calculate the economic contribution coefficient (ECC) and ecological carrying coefficient (ESC) of different years from 2000 to 2022 to reflect the spatial differentiation characteristics of carbon budget in different districts and counties.

[0079] The economic contribution coefficient (ECC) of carbon emissions reflects the size of regional carbon productivity and measures the differences in carbon emissions among counties in the region from an economic perspective. The calculation formula is as follows:

[0080]

[0081] In the formula, G i and G represent the GDP of the district, county and province respectively, C iand C represent the carbon emissions of the county and the province, respectively. ECC>1 indicates that the county's unit economic contribution rate is greater than its carbon emission contribution rate, and it has higher carbon emission economic efficiency and carbon productivity. In other words, the energy consumption per unit GDP is lower.

[0082] The carbon absorption ecological carrying coefficient (ESC) reflects the size of the regional carbon sequestration capacity, which is expressed as the quotient of the proportion of carbon absorption in the county to the province and the proportion of carbon emissions in the county to the province. The calculation formula is as follows:

[0083]

[0084] Where, CA i and CA represent the carbon absorption of a certain county and the whole province respectively, C i and C represent the carbon emissions of the county and the province respectively. ESC>1 indicates that the carbon absorption contribution rate of the county unit is greater than the carbon emission contribution rate, and has a higher carbon sink potential.

[0085] Step 3: Taking into account carbon emissions, carbon absorption, economic contribution coefficient and ecological carrying coefficient, the district and county units in the study area are divided into low-energy carbon sink area, high-energy carbon sink area, low-energy carbon emission area and high-energy carbon emission area according to the following division criteria (Table 4)

[0086] Table 4 Carbon partition characteristics

[0087]

[0088] Step 4: Based on the carbon balance zoning in step 3, combined with the existing main functional areas (national-level urbanized areas, provincial-level urbanized areas, national-level agricultural product production areas, and national-level key ecological functional areas), the "carbon balance zoning-main functional area" division results are constructed based on the GIS spatial overlay analysis method.

[0089] In other words, the present invention also discloses a method for dividing land space units that couples regional carbon balance and main functional areas. The technical solution requested for protection by the present invention can optimize the technical solution based on the land space division result, but the implementation of the present invention does not depend on optimizing the division result.

[0090] The above-mentioned national land space unit division uses the carbon emission, carbon absorption and GDP data in the study area to calculate the carbon emission economic contribution coefficient and the carbon absorption ecological carrying coefficient. Based on the economic contribution coefficient and the ecological carrying coefficient, the district and county units in the study area are preliminarily divided into low-energy carbon sink areas, high-energy carbon sink areas, low-energy carbon emission areas, and high-energy carbon emission areas; combined with the main functional zoning of the study area, based on the GIS spatial overlay analysis method, the carbon income and expenditure zoning results are superimposed and analyzed with the main functional areas to construct the "carbon income and expenditure zoning-main functional area" national land space unit division results.

[0091] The dual-carbon strategic goals and the optimization of the national land space pattern play a pivotal role in the promotion of ecological civilization construction and high-quality development in my country. However, the current division of national land space units and the contribution analysis of coupling regional carbon revenue and expenditure characteristics and main functional areas are still insufficient, which has become a key problem to be solved in the process of national land space planning and dual-carbon strategic practice.

[0092] The above method disclosed in the present invention first pre-processes the collected and collated data such as carbon emissions, carbon absorption and GDP, then calculates the economic contribution coefficient of carbon emissions and the ecological carrying coefficient of carbon absorption, and preliminarily carries out carbon revenue and expenditure zoning according to certain judgment criteria, and divides the study area into low-energy carbon sink area, high-energy carbon sink area, low-energy carbon emission area, and high-energy carbon emission area. This step helps to gain an in-depth understanding of the carbon revenue and expenditure status of each region, and can also provide strong support for the government to formulate targeted regional development policies. For example, in low-energy carbon sink areas, ecological advantages can be further exerted to promote green development; in high-energy carbon emission areas, it is necessary to strengthen carbon emission control and promote industrial transformation and upgrading. Then, the carbon revenue and expenditure zoning results of the study area are combined with the main functional zoning to construct the "carbon revenue and expenditure zoning-main functional area" land space unit division results, and a land space unit division method that couples regional carbon revenue and expenditure characteristics and main functional areas is obtained.

[0093] Finally, it should be noted that the above implementation examples are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to preferred examples, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing the spatiotemporal trend of vegetation net primary productivity and the contribution of climate and human beings, characterized by: It includes the following steps: Step (i) Conducting spatiotemporal trend analysis of vegetation NPP in different spatial units; Step (ii) Divide the vegetation net primary productivity NPP into: actual vegetation net primary productivity A NPP , potential vegetation net primary productivity P NPP and the net primary productivity of anthropogenic vegetation H NPP Three categories, and calculate the three categories of NPP data; Step (iii) analyzing the climate and human contribution of vegetation NPP in different spatial units based on the three types of NPP data obtained in step (ii).

2. The method for analyzing spatiotemporal trends of vegetation net primary productivity and analyzing climate and human contribution according to claim 1, characterized in that: The step (1) includes the following sub-steps: Sub-step 1-1: Data acquisition and preprocessing: Obtain vegetation net primary productivity (NPP) data and temperature and precipitation data in the study area from 2000 to 2022; Sub-step 1-2: Combined with the division of national land space units, the linear trend analysis method is used to analyze the interannual trend of vegetation NPP in different spatial units, and the NPP slope K is used to represent the temporal trend of NPP; Sub-step 1-3: Use a method combining the Hurst index and linear trend analysis to predict the future trend of net primary productivity of vegetation in different spatial units.

3. A method for analyzing spatiotemporal trends of vegetation net primary productivity and analyzing climate and human contribution according to claim 1 or 2, characterized in that: The data acquisition methods in step (ii) are: The actual net primary productivity of vegetation A NPP Obtained from MODIS products; The potential net primary productivity of vegetation P NPP represents the NPP affected only by climate change and is calculated as follows: P NPP =3000[1-e- 0 .0009695(V -20) ] <h2 style=";text-align:left;direction:ltr">L = 300 + 25T + 0.05T<h2 style=";text-align:left;direction:ltr"> 3 Wherein, V is evapotranspiration, in mm; R is precipitation, in mm; L is maximum evapotranspiration, in mm; T is temperature, in °C; The net primary productivity of anthropogenic vegetation H NPP NPP caused by human activities, NPP With A NPP The difference between is calculated.

4. A method for analyzing spatiotemporal trends of vegetation net primary productivity and analyzing climate and human contribution according to claim 1 or 2, characterized in that: The step (three) includes the following sub-steps: Sub-step 3-1: Calculate the three types of NPP slopes K according to the three types of NPP data obtained in step (ii); Sub-step 3-2: Use the three types of NPP slopes K to derive the impact of climate change and human activities on NPP: the degree of change in NPP is represented by ΔNPP; Sub-step 3-3: Based on the change degree of NPP ΔNPP, the contribution rate of climate change and human activities to NPP is obtained.

5. The method for analyzing spatiotemporal trends of vegetation net primary productivity and analyzing climate and human contribution according to claim 1, characterized in that: The method of using three types of NPP slopes K in step (iii) to evaluate the impact of climate change and human activities on NPP is: ΔNPP=(n-1)×K Where n is the total number of years, K is the slope of the NPP time series, and ΔNPP is the degree of change in NPP.

6. A method for analyzing spatiotemporal trends of vegetation net primary productivity and analyzing climate and human contribution according to claim 1 or 5, characterized in that: In the sub-step 3-3, according to A NPP Slope K a , P NPP Slope K p , H NPP Slope K h Identify 6 scenarios and NPP Change ΔNPP h and H NPP Change ΔNPP p The contribution rates of climate change and human activities under six scenarios were calculated respectively.

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