A method for analyzing spatiotemporal trends in vegetation net primary productivity and analyzing climate and human contributions

Abstract

The present invention discloses a method for analyzing the spatiotemporal trend of vegetation net primary productivity and analyzing the contribution of climate and human beings, comprising: step (1) performing spatiotemporal trend analysis of vegetation NPP in different spatial units; step (2) dividing 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 are selected and the three categories of NPP data are calculated; and step (3) analyzes the climate and human contributions to vegetation NPP in different spatial units based on the three categories of NPP data obtained in step (2). The present invention has the following beneficial effects: it conducts a spatiotemporal trend analysis of vegetation net primary productivity and quantifies the contribution characteristics of climate change and human activities, laying a solid foundation for a deep understanding of the changing characteristics and contribution patterns of ecosystem productivity, and promoting the implementation of the 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 contributions, 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 leading to significant gaps in the assessment of the contributions of climate change and human activities. For example, in some ecologically fragile regions, climate change may lead to reduced precipitation and increased temperatures, which could further exacerbate environmental problems and cause a significant decline in vegetation net primary productivity (NPP). Human activities such as overgrazing and irrational development may also exacerbate this trend. The interactions between these influencing factors are complex and difficult to quantify, making it a significant challenge to accurately isolate their contributions to changes in the carbon budget.

[0003] Residual analysis is computationally simple and has clear biological significance. It is a potential method for separating climate and human contributions and has been successfully applied in various regions. For example, in the prior art, scholars have elucidated the contributions of climate (42.35%) and humans (57.65%) to NPP on 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). Other 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 on 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). Furthermore, some researchers have concluded through residual analysis that humans and climate contribute 60.06% and 39.94% to NPP in China, 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 these studies focus on the regional level, and their specific spatial details are unclear. Therefore, a comprehensive analysis of the driving factors at the spatial unit scale is urgently needed.

[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 differentiated national land space planning and dual carbon strategy according to local conditions. Summary of the Invention

[0005] In response to the above-mentioned technical problems existing in the existing technology, the present invention provides a method for analyzing the spatiotemporal trends of vegetation net primary productivity 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 spatiotemporal trends in vegetation net primary productivity and analyzing climate and human contributions includes the following steps:

[0008] Step (1) conduct spatiotemporal trend analysis of vegetation NPP in different spatial units;

[0009] 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 the net primary productivity of anthropogenic vegetation H NPP Three categories, and calculate the three categories of NPP data;

[0010] Step (3) analyzing the climate and human contributions to vegetation NPP in different spatial units based on the three types of NPP data obtained in step (2).

[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, temperature, and precipitation data for the study area from 2000 to 2022;

[0013] Sub-step 1-2: Based on the division of national land space units, the interannual trend of vegetation NPP in different spatial units was analyzed using the linear trend analysis method, and the temporal trend of NPP was characterized by the NPP slope K;

[0014] Sub-steps 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 (2) 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] Where 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 is caused by human activities, NPP With A NPP The difference between them is calculated.

[0023] As an optimization solution, step (3) includes the following sub-steps:

[0024] Sub-step 3-1: Calculate the three types of NPP slopes K based on the three types of NPP data obtained in step (2);

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

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

[0027] Furthermore, the method for using the three types of NPP slopes K in step (3) 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] This study conducts a linear trend analysis of vegetation net primary productivity (NPP) across different spatial units to reveal its interannual variation. Combining this with the Hurst exponent, the study further predicts future NPP trends for each spatial unit. Subsequently, an effective method is proposed to quantify the impact of climate change and human activity on NPP, using six different scenarios to comprehensively assess the contributions of climate change and human activity to NPP changes across different spatial units. In summary, this study not only provides an in-depth understanding of the dynamics of vegetation net primary productivity but also, by quantifying the relative impacts of climate and human activity, provides a scientific basis for developing effective ecological protection and resource management strategies.

[0033] This study further analyzes the contribution of vegetation net primary productivity (NPP) in different spatial units based on linear trend analysis, 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 land space layout, and promoting regional sustainable development. This initiative can promote the formation of a more scientific and reasonable pattern of national land space development and protection, ensuring that while safeguarding economic and social development, we can effectively respond to the challenges of climate change and achieve a win-win situation for ecological and environmental protection and economic and social development.

[0034] In summary, in response to the current lack of NPP contribution analysis technology, this paper carried out a 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 figure is an overall flow chart of the method proposed in the present invention. DETAILED DESCRIPTION

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

[0038] This paper proposes a method for dividing the land space units and analyzing their contributions for coupling regional carbon budget and main functional zones, and explores the land space unit division strategy for coupling regional carbon budget and main functional zones based on the GIS spatial analysis method.

[0039] The present invention uses linear trend analysis, Hurst index and other methods to combine the results of national land space unit division with the net primary productivity of vegetation to analyze the changing trends of different spatial units. The Thornthwaite Memorial method is 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. This helps to improve the scientific nature 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, all data are converted to Albers projection, and the data are unified to 1 km and 1 year using the resampling method.

[0042] Sub-step 1-2: Based on the division of national land space units, the interannual trend of vegetation NPP in different spatial units is analyzed using a linear trend analysis method, and the temporal trend of NPP is characterized by the NPP slope K. The linear trend analysis calculation method proposed in this 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 mean NPP value of the study area in year t, K and b represent the fitting equation coefficients, and the slope K represents the temporal 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 using the following formula:

[0046]

[0047] The first term in the numerator first calculates the sum of the product 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 calculates 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 index reflects the persistence of the time series data set. The present invention uses this characteristic to adopt a method combining the Hurst index and linear trend analysis to predict the future trend of vegetation net primary productivity in different spatial units.

[0052] Specifically, the Hurst index is between 0 and 1. 0 < H < 0.5 indicates that the future NPP is opposite 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 land space unit division in step 1, the present invention adopts a method combining the Hurst index 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 Continued 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 (two) 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 three types of data are obtained 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 impact 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, P 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] Where 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 is 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 that human activities have led to a decrease in NPP, while a negative value indicates an increase in NPP.

[0066] Step (3) Analyze the climate and human contribution of vegetation NPP in different spatial units based on the three types of NPP data obtained in step (2)

[0067] Sub-step 3-1: Calculate the three types of NPP slopes K based on the three types of NPP data obtained in step (2); that is, the actual NPP (A) calculated in step (2) NPP ), potential NPP (P NPP ) and anthropogenic NPP (H NPP ) based on the linear trend analysis method provided in step (1), the 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 impacts of climate change and anthropogenic activities on NPP (i.e., use the slopes to evaluate the impacts of climate change and anthropogenic 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 in NPP ΔNPP, the contribution rates of climate change and human activities to NPP are 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 contributions 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 Change.

[0076] In addition, the spatiotemporal trend analysis of vegetation NPP in different spatial units conducted in step (1) of the present invention may further include: first, optimizing the spatial unit division. The present invention discloses the following spatial unit division optimization method, specifically a national land spatial unit division method that couples carbon budget and major functional zones:

[0077] Step 1: Data Acquisition and Preprocessing: Carbon emissions, carbon absorption, GDP data, major functional zoning, and vegetation net primary productivity (NPP) data for each district and county in the study area from 2000 to 2022 were obtained. Where carbon absorption data for certain counties was missing, the missing data was supplemented by calculating the mean of adjacent counties. To ensure data timeliness, MODIS NPP data were also collected and linearly fitted to the carbon absorption data, extending the carbon absorption data to 2022. Carbon emission data included carbon dioxide emissions from fossil fuels and biofuels. These two were summed to form total emissions, and then regionally summarized to obtain carbon emission data for each county in the study area from 2000 to 2022. Carbon emission, carbon absorption, and GDP data for each district and county in the study area for different years were organized in separate Excel spreadsheets, visualized using ArcGIS, and further analyzed through data linking.

[0078] Step 2: Calculate the economic contribution coefficient (ECC) and ecological carrying coefficient (ESC) for 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] Where G i and G represent the GDP of the district and county and the province respectively, C iand C represent the carbon emissions of the district and county and the province, respectively. An ECC greater than 1 indicates that the district or county's unit economic contribution rate is greater than its contribution rate to carbon emissions, indicating high carbon emission economic efficiency and carbon productivity. In other words, the energy consumption per unit of GDP is low.

[0082] The ecological carrying capacity of carbon absorption (ESC) reflects the size of the regional carbon sequestration capacity. It is expressed as the quotient of the proportion of carbon absorption in a 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 entire province respectively, C i and C represent the carbon emissions of the county and the province, respectively. An ESC > 1 indicates that the county's contribution to carbon absorption is greater than its contribution to carbon emissions, indicating a high 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 areas, high-energy carbon sink areas, low-energy carbon emission areas and high-energy carbon emission areas according to the following division criteria (Table 4)

[0086] Table 4 Carbon partition characteristics

[0087]

[0088] Step 4: Based on the carbon budget zoning in step 3, combined with the existing main functional zones (national-level urbanized areas, provincial-level urbanized areas, national-level agricultural product production areas, and national-level key ecological functional zones), the "carbon budget zoning-main functional zone" 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 zones. 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 land space unit division uses the carbon emission, carbon absorption and GDP data in the study area to calculate the economic contribution coefficient of carbon emission and the ecological carrying coefficient of carbon absorption. 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" land space unit division results.

[0091] The dual-carbon strategy and the optimization of national land space play a crucial role in promoting ecological civilization and high-quality development in my country. However, the current delineation of national land space units and the analysis of their contributions, coupled with regional carbon budget characteristics and major functional zones, remain insufficient, becoming a key challenge that urgently needs to be addressed in national land space planning and the implementation of the dual-carbon strategy.

[0092] The above-mentioned method disclosed in the present invention first pre-processes the collected and collated data such as carbon emissions, carbon absorption and GDP, and then calculates the economic contribution coefficient of carbon emissions and the ecological carrying coefficient of carbon absorption, and preliminarily carries out carbon budget zoning according to certain judgment criteria, dividing 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 budget 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, we can further play the ecological advantages and promote green development; in high-energy carbon emission areas, we need to strengthen carbon emission control and promote industrial transformation and upgrading. Then, the carbon budget zoning results of the study area are combined with the main functional zoning to construct the "carbon budget zoning-main functional area" land space unit division results, and a land space unit division method that couples regional carbon budget 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 solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to preferred examples, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing spatiotemporal trends in vegetation net primary productivity and analyzing climate and human contributions, characterized by: It includes the following steps: Step (1) Conduct spatiotemporal trend analysis of vegetation NPP in different spatial units; Step 2: Divide the net primary productivity (NPP) of vegetation into: actual 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 (3) analyzing the climate and human contributions to vegetation NPP in different spatial units based on the three types of NPP data obtained in step (2); 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: Based on the division of national land space units, the interannual trend of vegetation NPP in different spatial units was analyzed using the linear trend analysis method, and the temporal trend of NPP was characterized by the NPP slope K; Sub-steps 1-3: Use a combination of the Hurst index and linear trend analysis to predict the future trend of vegetation net primary productivity in different spatial units; The data acquisition methods in step (2) 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: Where 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 is caused by human activities, NPP With A NPP The difference between them is calculated; The step (3) includes the following sub-steps: Sub-step 3-1: Calculate the three types of NPP slopes K based on the three types of NPP data obtained in step (2); Sub-step 3-2: Use the three types of NPP slopes K to derive the impact of climate change and anthropogenic activities on NPP: the degree of change in NPP is expressed as ΔNPP; Sub-step 3-3: Based on the change in NPP ΔNPP, the contribution rates of climate change and human activities to NPP are obtained.

2. The method for analyzing spatiotemporal trends in vegetation net primary productivity and analyzing climate and human contributions according to claim 1, characterized in that: The method for using the three types of NPP slopes K in step (3) to evaluate the impact of climate change and human activities on NPP is: 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.

3. A method for analyzing spatiotemporal trends in vegetation net primary productivity and analyzing climate and human contributions according to claim 1 or 2, 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 ∆NPP h and H NPP Change ∆NPP p The contribution rates of climate change and human activities under six scenarios were calculated respectively.

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