Method, device and computer-readable medium for arid region vegetation response to climate change

Abstract

The present invention discloses a method, a device and a computer-readable medium for the response of vegetation in arid areas to climate change, which belongs to the field of physical geography technology. The implementation of the method includes the following steps: S1, acquisition and preprocessing of vegetation, climate and other data in arid and semi-arid areas; S2, definition of vegetation quality index and climate factor index; S3, calculation of vegetation quality index and climate factor index data set in arid and semi-arid areas; S4, construction of a vegetation response method model to climate change based on spatial position sequence; S5, exploration of vegetation response mechanism to climate change based on spatial position sequence. The present invention can strengthen the analysis of vegetation response mechanism to climate change, improve the timeliness and effectiveness of environmental management system and response strategy, promote rapid response and adjustment at policy and practice level, so as to more effectively respond to the impact of climate change on vegetation in arid and semi-arid areas.

Description

Technical Field

[0001] The present invention relates to the field of physical geography technology, and in particular to a method, a device and a computer-readable medium for responding vegetation in arid areas to climate change. Background Art

[0002] Vegetation, as an important component of terrestrial ecosystems, is a natural link between soil, atmosphere and water. Climate is a key factor affecting plant growth and development, and vegetation is particularly sensitive to climate change, especially water and heat conditions. China's arid and semi-arid regions are the world's major climate change sensitive areas and ecologically fragile areas, and the vegetation coverage in this region shows a strip-shaped spatial distribution pattern that decreases from southeast to northwest. The vegetation types are cultivated vegetation, coniferous forests, meadows, shrubs, grasslands and alpine vegetation. As the global climate develops towards warming and humidification, the vegetation in this region is significantly affected by climate change. However, the current assessment methods and technical systems for the response of vegetation in arid and semi-arid regions to climate change generally rely on pixel-by-pixel analysis strategies, and explore their correlation by calculating the Pearson correlation coefficient between the time series remote sensing image data of vegetation and climate factors. This method fails to utilize the natural laws and complexity of vegetation spatial distribution, especially ignoring the unique response pattern of vegetation in arid and semi-arid regions to climate change. Therefore, the response results obtained by this method are often difficult to fully and accurately characterize the response mechanism of vegetation in the region to climate change, which in turn limits the timeliness and effectiveness of relevant environmental management systems and response strategies, and causes lags in policy and practice.

[0003] Therefore, there is an urgent need to develop a new technical method that takes into account the spatial distribution of vegetation, strengthens the analysis of the response mechanism of vegetation to climate change, and provides objective and accurate patterns of the response of vegetation to climate change in the region. Summary of the invention

[0004] The technical task of the present invention is to address the above deficiencies and provide a method, device and computer-readable medium for the response of vegetation in arid areas to climate change, which can strengthen the analysis of the response mechanism of vegetation to climate change, improve the timeliness and effectiveness of environmental management systems and response strategies, and promote rapid response and adjustment at the policy and practice levels, so as to more effectively respond to the impact of climate change on vegetation in arid and semi-arid areas.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A method for the response of vegetation in arid areas to climate change, the implementation of which comprises the following steps:

[0007] S1. Acquisition and preprocessing of vegetation, climate and other data in arid and semi-arid areas:

[0008] Acquire remote sensing satellite data of arid and semi-arid areas for many years, including fractional vegetation cover (FVC), leaf area index (LAI), gross primary productivity (GPP), temperature, precipitation, and vegetation type, and pre-process various remote sensing data including radiation calibration, atmospheric correction, and spatial correction to form a data set;

[0009] S2. Define vegetation quality indicators and climate factor indicators:

[0010] The vegetation quality index (VQI) was constructed through the fractional vegetation cover (FVC), leaf area index (LAI) and gross primary productivity (GPP) as an evaluation index to reflect the characteristics of regional vegetation ecological quality changes; based on the vegetation growth mechanism, climate factor indicators were designed, including: accumulated temperature data from April to June, day and night temperature difference from June to September, accumulated precipitation from April to June and average precipitation from July to August;

[0011] S3. Calculate the vegetation quality index and climate factor index data set in arid and semi-arid areas:

[0012] The mean synthesis method was used to obtain the vegetation ecological parameter data of the vegetation growing season from June to September, and the mean synthesis method was used to obtain the annual time series data set of accumulated temperature data from April to June, the day-night temperature difference from June to September, the accumulated precipitation from April to June, and the average precipitation from July to August;

[0013] S4. Construct a model of vegetation response to climate change based on spatial position sequence:

[0014] The images of vegetation and climate change over the past few years are spatially superimposed to explore the impact of climate change on vegetation through the relationship between the two in spatial position sequence;

[0015] S5. Explore the response mechanism of vegetation to climate change based on spatial position sequences.

[0016] This method can make good use of the natural laws and complexity of vegetation spatial distribution, especially the strip-shaped spatial distribution pattern unique to vegetation in arid and semi-arid areas, and comprehensively and accurately depict the response mechanism of vegetation in the region to climate change.

[0017] Furthermore, the multi-year remote sensing satellite data are remote sensing satellite data for arid and semi-arid areas for 20 years from 2002 to 2021.

[0018] Furthermore, the step 3 is specifically as follows:

[0019] Select the data of the growing season from June to September, obtain all the image data of vegetation coverage (FVC), leaf area index (LAI), and primary productivity (GPP) from June to September of the required year through the platform, use the mean synthesis method to obtain the annual image data set of each ecological parameter, use the NDVI data to calculate the annual image data set of vegetation coverage, and resample all the data to 1km spatial resolution; select the maximum value of each ecological parameter in the study area as the reference value, calculate the ratio of each ecological parameter to its reference value in turn, obtain the relative density image of the ecological parameter in the study area, and then calculate the vegetation quality index:

[0020] ,

[0021] In the formula, For the first The relative density of annual ecological parameters, For the Annual ecological parameters, For the Annual maximum values ​​of ecological parameters;

[0022] ,

[0023] In the formula, For the Annual remote sensing vegetation index, For the The relative density of vegetation coverage in the year, For the Annual leaf area index relative density, For the Relative density of annual total primary productivity.

[0024] Furthermore, the image data is obtained through a Google Earth Engine (GEE) platform, and the data operation is completed in the Google Earth Engine (GEE) platform.

[0025] Further, the selection of vegetation quality indicators and climate factor indicators:

[0026] In order to more precisely characterize the quality of natural vegetation in an ecosystem and reflect the overall status of vegetation in the ecosystem and the ecosystem, the vegetation quality index (VQI) was constructed by using fractional vegetation cover (FVC), leaf area index (LAI) and gross primary productivity (GPP) as evaluation indicators to reflect the quality of regional vegetation.

[0027] Since the asynchronous changes in day and night temperatures have different effects on vegetation activities, and climate change may have a time effect (hysteresis, accumulation) on vegetation, this method designs four climate indicators based on the physiological response mechanism of vegetation to climate change. The accumulated temperature from April to June and the temperature difference between day and night from June to September respond to the optimal growth temperature of vegetation and the accumulation level of vegetation organic matter in turn; and the accumulated precipitation from April to June and the average precipitation from July to August are closely related to the germination of vegetation during the green period and the development of plant growth period in turn.

[0028] Furthermore, the fractional vegetation cover (FVC) data uses the MOD13A1 remote sensing image of the NASA database, the leaf area index (LAI) uses the MCD15A3H remote sensing image of the NASA database, the gross primary productivity (GPP) uses the MOD17A2H remote sensing image of the NASA database, the temperature data uses the MOD11A1 data product of the NASA database, and the precipitation data uses the CHIRPS precipitation product.

[0029] Furthermore, the S4 step is specifically as follows:

[0030] The images of vegetation and climate change over the past few years are spatially superimposed, and the impact of climate change on vegetation is explored through the relationship between the two in the spatial position sequence, which includes the following steps:

[0031] Firstly, the changing trends of vegetation quality indexes and climate indexes over many years at the spatial position sequence points are calculated;

[0032] Secondly, the Pearson correlation coefficient of the changing trend values ​​of vegetation quality indicators and climate indicators in the spatial position sequence was calculated to obtain the impact of climate change on vegetation, that is, the response law of vegetation quality to climate change.

[0033] Furthermore, the Theil-Sen median trend analysis method was used to quantify the degree of change in vegetation quality over the past 20 years, and a point-line graph was made based on the characteristic line to extract the degree of change in vegetation quality over the past 20 years, and to explore the spatial distribution of the degree of change in vegetation quality;

[0034] The Theil-Sen median trend analysis method was used to calculate the degree of change of vegetation quality index (VQI) and various climate factors in 20 years. The degree of change of vegetation quality index VQI and the degree of change of various climate factors were extracted according to the characteristic line to make a point-line graph, and the Pearson correlation analysis method was used to explore the impact of climate change on vegetation greening: ,

[0035] In the formula, is the Pearson correlation coefficient, for and The covariance of the variables, for The standard deviation of the variable, for Expectations of variables;

[0036] The Pearson correlation coefficient can describe the strength of the linear correlation between two variables.

[0037] The present invention also claims a device for protecting the response of vegetation in arid areas to climate change, comprising: at least one memory and at least one processor;

[0038] The at least one memory is used to store a machine-readable program;

[0039] The at least one processor is used to call the machine-readable program to implement the above method.

[0040] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0041] Compared with the prior art, the method, device and computer-readable medium for the response of vegetation in arid areas to climate change of the present invention have the following beneficial effects:

[0042] 1. Comprehensive utilization of spatial distribution laws: This invention fully utilizes the natural laws and complexity of vegetation spatial distribution by constructing an analysis model based on spatial position sequences, especially the strip-shaped spatial distribution pattern unique to arid and semi-arid areas, and can more accurately reflect the response mechanism of vegetation to climate change. This method overcomes the limitation of the existing pixel-by-pixel analysis strategy that cannot fully present the spatial distribution characteristics of vegetation.

[0043] 2. High-precision response law characterization: The Theil-Sen median trend analysis method and Pearson correlation analysis method are used to more accurately calculate the change trend and correlation of vegetation quality indicators and climate factor indicators. Theil-Sen median trend analysis method is a robust non-parametric trend calculation method that can effectively reduce the interference of data outliers and improve the accuracy of long-term series data analysis.

[0044] 3. Selection of climate factors consistent in time and space: The climate factor indicators designed by the present invention include accumulated temperature, temperature difference between day and night, accumulated precipitation and average precipitation, which fully consider the climate conditions at different stages of vegetation growth. By selecting data from the vegetation growth season from June to September, the consistency of vegetation quality indicators and climate factor indicators in time and space is ensured, which improves the reliability and scientificity of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1It is a flowchart of a method for the response of vegetation in arid areas to climate change provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with specific embodiments.

[0047] The embodiment of the present invention provides a method for vegetation in arid areas to respond to climate change. The implementation of the method includes the following steps:

[0048] S1. Acquisition and preprocessing of vegetation, climate and other data in arid and semi-arid areas:

[0049] Obtain remote sensing satellite data such as fractional vegetation cover (FVC), leaf area index (LAI), gross primary productivity (GPP), temperature, precipitation, vegetation type, etc. in arid and semi-arid areas for 20 years, and perform radiometric calibration, atmospheric correction, spatial correction and other preprocessing on various remote sensing data to form a data set;

[0050] S2. Define vegetation quality indicators and climate factor indicators:

[0051] The vegetation quality index (VQI) was constructed through the fractional vegetation cover (FVC), leaf area index (LAI) and gross primary productivity (GPP) as an evaluation index to reflect the characteristics of regional vegetation ecological quality changes; based on the vegetation growth mechanism, four climate factor indicators were designed, namely: accumulated temperature data from April to June, day and night temperature difference from June to September, accumulated precipitation from April to June and average precipitation from July to August;

[0052] S3. Calculate the vegetation quality index and climate factor index data set in arid and semi-arid areas:

[0053] The mean synthesis method was used to obtain the vegetation ecological parameter data of the vegetation growing season from June to September, and the mean synthesis method was used to obtain the annual time series data set of accumulated temperature data from April to June, the day-night temperature difference from June to September, the accumulated precipitation from April to June, and the average precipitation from July to August;

[0054] S4. Construct a model of vegetation response to climate change based on spatial position sequence:

[0055] The images of vegetation and climate change over the past few years are spatially superimposed to explore the impact of climate change on vegetation through the relationship between the two in spatial position sequence;

[0056] S5. Explore the response mechanism of vegetation to climate change based on spatial position sequences.

[0057] Wherein, the step 3 is specifically as follows:

[0058] In order to better present the vegetation quality status, data from the growing season from June to September were selected, and all image data of vegetation coverage (FVC), leaf area index (LAI), and primary productivity (GPP) from June to September 2002 to 2021 were obtained through the Google Earth Engine (GEE) platform. The mean synthesis method was used to obtain the annual image dataset of each ecological parameter, and the NDVI data was used to calculate the annual image dataset of vegetation coverage FVC, and all data were resampled to a spatial resolution of 1 km; the maximum value of each ecological parameter in the study area was selected as the reference value, and the ratio of each ecological parameter to its reference value was calculated in turn to obtain the relative density image of the ecological parameter in the study area, and then the vegetation quality index was calculated:

[0059] ,

[0060] In the formula, For the first The relative density of annual ecological parameters, For the Annual ecological parameters, For the Annual maximum values ​​of ecological parameters;

[0061] ,

[0062] In the formula, For the Annual remote sensing vegetation index, For the The relative density of vegetation coverage in the year, For the Annual leaf area index relative density, For the Relative density of annual total primary productivity.

[0063] The above operations are all completed in the Google Earth Engine (GEE) platform.

[0064] Selection of vegetation quality indicators and climate factor indicators:

[0065] In order to more precisely characterize the quality of natural vegetation in an ecosystem and reflect the overall status of vegetation in the ecosystem and the ecosystem, the vegetation quality index (VQI) was constructed by using fractional vegetation cover (FVC), leaf area index (LAI) and gross primary productivity (GPP) as evaluation indicators to reflect the quality of regional vegetation.

[0066] Since the asynchronous changes in day and night temperatures have different effects on vegetation activities, and climate change may have a time effect (hysteresis, accumulation) on vegetation, this method designed four climate indicators based on the physiological response mechanism of vegetation to climate change, namely, accumulated temperature from April to June, day and night temperature difference from June to September, which respond to the optimal growth temperature of vegetation and the accumulation level of vegetation organic matter in turn; and accumulated precipitation from April to June and average precipitation from July to August, which are closely related to the germination of vegetation during the green period and the development of plant growth period in turn.

[0067] The fractional vegetation cover (FVC) data uses the MOD13A1 remote sensing image of the NASA database, the leaf area index (LAI) uses the MCD15A3H remote sensing image of the NASA database, the gross primary productivity (GPP) uses the MOD17A2H remote sensing image of the NASA database, the temperature data uses the MOD11A1 data product of the NASA database, and the precipitation data uses the CHIRPS precipitation product.

[0068] The S4 step is specifically as follows:

[0069] The spatial superposition of vegetation and climate change images over the years is used to explore the impact of climate change on vegetation through the relationship between the two in the spatial position sequence. This includes the following two steps:

[0070] Firstly, the variation trends of vegetation quality indexes and climate indexes in the spatial sequence points over the past 20 years are calculated;

[0071] Secondly, the Pearson correlation coefficient of the changing trend values ​​of vegetation quality indicators and climate indicators in the spatial position sequence was calculated to obtain the impact of climate change on vegetation, that is, the response law of vegetation quality to climate change.

[0072] This method constructs spatial position sequence data through a characteristic line that runs through the study area. Since the vegetation in China's arid and semi-arid areas presents a regular northeast-southwest strip distribution pattern, the characteristic line perpendicular to the strip direction is more representative of the situation in the study area. The Hu Huanyong Line is selected as the reference line to determine the characteristic line. The center of gravity of the data is calculated year by year through the vegetation quality index (VQI) data of the growing season (June to September) from 2002 to 2021, and then the average center of gravity of 20 years is calculated. The perpendicular line of the Hu Huanyong Line is made through the average center of gravity as the characteristic line of this article.

[0073] This method selects the vegetation quality index (VQI) data of each growing season (June to September) from 2002 to 2021, and uses Google Earth Engine (GEE) to calculate the centroid of each year's VQI data. The centroid refers to the geometric center of the vegetation quality index in the entire study area. The centroid data of each year is exported and its specific longitude and latitude coordinates are recorded. The centroid data calculated each year is imported into ArcGIS. Use the "Mean Center" tool in ArcGIS to calculate the centroid data from 2002 to 2021 to obtain the average centroid for 20 years. Mark the location of the average centroid on the map and record its longitude and latitude. Then, in ArcGIS, starting from the average centroid, draw a straight line perpendicular to the Hu Huanyong Line, that is, the characteristic line. This characteristic line should run through the study area and represent the spatial distribution law of vegetation.

[0074] Next, import and process the VQI and climate indicators (such as temperature and precipitation) data from 2002 to 2021. Use the Theil-Sen median trend analysis method in Python, which is a robust non-parametric trend calculation method that is not affected by data outliers and is suitable for trend analysis of long-term series data. First, perform data processing and calculations in Python. Using the Theil-Sen median trend analysis method, calculate the 20-year trend of vegetation quality indicators and climate indicators at each spatial position sequence point. Export the change trend data of each spatial position sequence point, including the vegetation quality change trend and the climate indicator change trend. Then, import these change trend data into ArcGIS. Use the point-line graph tool in ArcGIS to extract the change trend data of each spatial position sequence point according to the characteristic line, generate a point-line graph, and show the spatial distribution of the vegetation quality change trend and the climate indicator change trend.

[0075] After that, select the VQI change trend and climate indicator change trend data of each spatial position sequence point on the characteristic line. Import these data into Python for further analysis. Select the Pearson correlation analysis method, which can explore the linear correlation between two variables and is suitable for analyzing the relationship between vegetation and climate change. In Python, calculate the Pearson correlation coefficient of the VQI change trend and the climate indicator change trend of each spatial position sequence point on the characteristic line. Export the correlation coefficient data of each spatial position sequence point, including the correlation coefficient between VQI and temperature change, and VQI and precipitation change. Import the correlation coefficient data exported from Python into ArcGIS. In ArcGIS, use spatial analysis tools to visualize the correlation coefficient data and generate a spatial distribution map to show the correlation between vegetation quality change trends and climate change trends.

[0076] The specific implementation of this method is as follows:

[0077] Step 1: Acquisition and preprocessing of vegetation, climate and other data in arid and semi-arid areas:

[0078] Obtain remote sensing satellite data such as fractional vegetation cover (FVC), leaf area index (LAI), gross primary productivity (GPP), temperature, precipitation, and vegetation type in China's arid and semi-arid areas from 2002 to 2021, and perform pre-processing such as radiation calibration, atmospheric correction, and spatial correction on various types of remote sensing data to form a data set.

[0079] Step 2: Define vegetation quality indicators and climate factor indicators:

[0080] The vegetation quality index (VQI) was constructed through vegetation coverage (FVC), leaf area index (LAI), and gross primary productivity (GPP) as an evaluation index to reflect the characteristics of regional vegetation ecological quality changes. The maximum value of each ecological parameter (vegetation coverage (FVC), leaf area index (LAI), and gross primary productivity (GPP)) was selected as the reference value, and the ratio of each ecological parameter to its reference value was calculated in turn to obtain the relative density of the ecological parameter in the study area, and then the vegetation quality index was calculated. The specific formula is as follows:

[0081] ,

[0082] In the formula, For the first The relative density of annual ecological parameters, For the Annual ecological parameters, For the Annual maximum values ​​of ecological parameters;

[0083] ,

[0084] In the formula, For the Annual remote sensing vegetation index, For the The relative density of vegetation coverage in the year, For the Annual leaf area index relative density, For the Relative density of annual total primary productivity.

[0085] Based on the vegetation growth mechanism, four climate factor indicators are designed, namely, the accumulated temperature data from April to June reflects the optimal growth temperature of vegetation, and the day-night temperature difference from June to September reflects the accumulation level of vegetation organic matter; two precipitation indicators, namely, the cumulative precipitation from April to June and the average precipitation from July to August, are closely related to the germination of vegetation during the greening period and the development of plants during the growth period.

[0086] Step 3: Calculate the vegetation quality index and climate factor index data set in arid and semi-arid areas:

[0087] In order to better present the spatiotemporal changes of vegetation, vegetation data from the vegetation growing season from June to September were selected. Based on S1, the vegetation coverage (FVC), leaf area index (LAI), and primary productivity (GPP) data sets from 2002 to 2021 were obtained, and the mean synthesis method was used to obtain the vegetation ecological parameter data from the vegetation growing season from June to September. According to the vegetation quality index calculation method in step 2, the vegetation quality index of China's arid and semi-arid areas was calculated year by year to obtain its annual time series data set.

[0088] Based on step 1, the temperature and precipitation remote sensing satellite data from 2002 to 2021 are obtained, and the mean synthesis method is used to obtain the annual time series data set of accumulated temperature data from April to June, day and night temperature difference from June to September, accumulated precipitation from April to June, and average precipitation from July to August.

[0089] Step 4: Construct a model of vegetation response to climate change based on spatial position sequence:

[0090] The main idea of ​​the vegetation response to climate change method based on spatial position sequence is to spatially superimpose the images of vegetation and climate change over the years, and explore the impact of climate change on vegetation through the relationship between the two in the spatial position sequence. It includes two steps: first, calculate the changing trend of vegetation quality indicators and climate indicators at the spatial position sequence points for 20 years; second, calculate the Pearson correlation coefficient of the changing trend values ​​of vegetation quality indicators and climate indicators in the spatial position sequence, and obtain the impact of climate change on vegetation, that is, the response law of vegetation quality to climate change.

[0091] This method constructs spatial position sequence data through a characteristic line that runs through the study area. Since the vegetation in China's arid and semi-arid areas presents a regular northeast-southwest strip distribution pattern, the characteristic line perpendicular to the strip direction is more representative of the situation in the study area. The Hu Huanyong Line is selected as the reference line to determine the characteristic line. The center of gravity of the data is calculated year by year through the vegetation quality index (VQI) data of the growing season (June to September) from 2002 to 2021, and then the average center of gravity of 20 years is calculated. The perpendicular line of the Hu Huanyong Line is made through the average center of gravity as the characteristic line of this article. There are two reasons for choosing the Hu Huanyong Line as the reference line. First, the direction of the Hu Huanyong Line is consistent with the direction of the vegetation distribution strip. Second, the subsequent analysis can be quantified with the help of its vertical distance from the Hu Huanyong Line.

[0092] The Theil-Sen median trend analysis method was used to quantify the degree of change in vegetation quality over 20 years. The degree of change in vegetation quality over 20 years was extracted based on the characteristic lines to make a point-line graph and explore the spatial distribution of the degree of change in vegetation quality.

[0093] Theil-Sen median trend analysis is a robust nonparametric trend calculation method. Compared with the linear regression analysis method, this method does not require the data to follow a certain distribution and can effectively reduce the interference of data outliers. Currently, Theil-Sen median trend analysis is an important method for determining the trend of long-term series data, and has been widely used in long-term series analysis of vegetation:

[0094] ,

[0095] In the formula, For data change trends, To calculate the median of the data, For input data. If it is greater than 0, it means that the vegetation is greening, otherwise it is browning.

[0096] Theil-Sen median trend analysis method was used to calculate the change degree of vegetation quality index (VQI) and various climate factors in 20 years. The change degree of VQI and the change degree of various climate factors were extracted according to the characteristic lines to make a point-line graph, and the Pearson correlation analysis method was used to explore the impact of climate change on vegetation greening.

[0097] The Pearson correlation analysis method can explore the relationship between vegetation and climate in different geographical locations. This method can describe the strength of the linear correlation between two variables:

[0098] ,

[0099] In the formula, is the Pearson correlation coefficient, for and The covariance of the variables, for The standard deviation of the variable, for Expectations of variables.

[0100] Step 5: Explore the response mechanism of vegetation to climate change based on spatial position sequence:

[0101] Based on the dataset of vegetation quality indicators and climate factor indicators in arid and semi-arid areas of China obtained in step 3, the method model of vegetation response to climate change based on spatial position sequence constructed in step 4 is used to calculate the response mechanism of vegetation in arid and semi-arid areas of China to climate change including spatial distribution rules.

[0102] Existing assessment methods mainly rely on pixel-by-pixel analysis methods, which explore the correlation by calculating the Pearson correlation coefficient between the time-series remote sensing image data of vegetation and climate factors, but fail to fully and accurately reflect the unique response pattern of the strip spatial distribution pattern unique to arid and semi-arid areas to climate change. The response results caused by such methods often make it difficult to fully and accurately describe the response mechanism of vegetation in the region to climate change, thereby limiting the timeliness and effectiveness of relevant environmental management systems and response strategies. The calculation method of vegetation response to climate change based on the spatial distribution position of vegetation provided by this method can strengthen the analysis of vegetation response mechanism to climate change, improve the timeliness and effectiveness of environmental management systems and response strategies, and promote rapid response and adjustment at the policy and practice levels, so as to more effectively respond to the impact of climate change on vegetation in arid and semi-arid areas of China.

[0103] An embodiment of the present invention also provides a device for arid area vegetation to respond to climate change, comprising: at least one memory and at least one processor;

[0104] The at least one memory is used to store a machine-readable program;

[0105] The at least one processor is used to call the machine-readable program to implement the method for arid area vegetation to respond to climate change as described in the above embodiment.

[0106] The embodiment of the present invention further provides a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the method for the response of vegetation in arid areas to climate change described in the above embodiment is implemented. Specifically, a system or device equipped with a storage medium can be provided, on which software program codes implementing the functions of any of the above embodiments are stored, and a computer (or CPU or MPU) of the system or device is enabled to read and execute the program code stored in the storage medium.

[0107] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0108] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0109] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0110] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0111] The present invention is shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A method for the response of vegetation in arid areas to climate change, characterized in that: The implementation of this method includes the following steps: S1. Acquisition and preprocessing of vegetation, climate and other data in arid and semi-arid areas: Acquire remote sensing satellite data of arid and semi-arid areas for many years, including vegetation coverage, leaf area index, gross primary productivity, temperature, precipitation, and vegetation type, and pre-process various types of remote sensing data, including radiation calibration, atmospheric correction, and spatial correction, to form a data set; S2. Define vegetation quality indicators and climate factor indicators: Vegetation quality indexes were constructed through vegetation coverage, leaf area index, and total primary productivity as evaluation indicators to reflect the changing characteristics of regional vegetation ecological quality; based on the vegetation growth mechanism, climate factor indicators were designed, including: accumulated temperature data from April to June, day and night temperature difference from June to September, accumulated precipitation from April to June, and average precipitation from July to August; S3. Calculate the vegetation quality index and climate factor index data set in arid and semi-arid areas: The mean synthesis method was used to obtain the vegetation ecological parameter data of the vegetation growing season from June to September; the mean synthesis method was used to obtain the annual time series data set of accumulated temperature data from April to June, the day-night temperature difference from June to September, the accumulated precipitation from April to June, and the average precipitation from July to August; S4. Construct a model of vegetation response to climate change based on spatial position sequence: The images of vegetation and climate change over the past few years are spatially superimposed to explore the impact of climate change on vegetation through the relationship between the two in spatial position sequence; S5. Explore the response mechanism of vegetation to climate change based on spatial position sequence; Wherein, the S4 step is specifically as follows: The images of vegetation and climate change over the past few years are spatially superimposed, and the impact of climate change on vegetation is explored through the relationship between the two in the spatial position sequence, which includes the following steps: Firstly, the changing trends of vegetation quality indexes and climate indexes over many years at the spatial position sequence points are calculated; Secondly, the Pearson correlation coefficient of the trend values ​​of vegetation quality indicators and climate indicators in the spatial position sequence is calculated to obtain the impact of climate change on vegetation, that is, the response law of vegetation quality to climate change; The Theil-Sen median trend analysis method was used to quantify the degree of change in vegetation quality over 20 years. The degree of change in vegetation quality over 20 years was extracted based on the characteristic lines to make a point-line graph and explore the spatial distribution of the degree of change in vegetation quality. The Theil-Sen median trend analysis method was used to calculate the degree of change of vegetation quality indicators and various climate factors over 20 years. The degree of change of vegetation quality indicators and various climate factors was extracted according to the characteristic lines to make a point-line graph, and the Pearson correlation analysis method was used to explore the impact of climate change on vegetation greening: ； In the formula, is the Pearson correlation coefficient, for and The covariance of the variables, for The standard deviation of the variable, for Expectations of variables; The Pearson correlation coefficient can describe the strength of the linear correlation between two variables.

2. The method for arid area vegetation response to climate change according to claim 1, characterized in that: The multi-year remote sensing satellite data mentioned above are remote sensing satellite data for arid and semi-arid areas for 20 years from 2002 to 2021.

3. The method for arid area vegetation response to climate change according to claim 1, characterized in that: The step S3 is specifically: Select the data of the growing season from June to September, and obtain all the image data of vegetation coverage, leaf area index, and total primary productivity from June to September of the required year. Use the mean synthesis method to obtain the annual image dataset of each ecological parameter. Use NDVI data to calculate the annual image dataset of vegetation coverage, and resample all data to a spatial resolution of 1 km. The maximum value of each ecological parameter in the study area was selected as the reference value, and the ratio of each ecological parameter to its reference value was calculated in turn to obtain the relative density image of the ecological parameter in the study area, and then the vegetation quality index was calculated: ； In the formula, For the The relative density of annual ecological parameters, For the Annual ecological parameters, For the Annual maximum values ​​of ecological parameters; ； In the formula, For the Annual remote sensing vegetation index, For the The relative density of vegetation coverage in the year, For the Annual leaf area index relative density, For the Relative density of annual total primary productivity.

4. The method for arid area vegetation response to climate change according to claim 3, characterized in that: The image data is obtained through the Google Earth Engine platform, and the data operation is completed in the Google Earth Engine platform.

5. The method for arid area vegetation response to climate change according to claim 1 or 3, characterized in that: Selection of vegetation quality indicators and climate factor indicators: The vegetation quality index is constructed by selecting vegetation coverage, leaf area index and total primary productivity as evaluation indicators to reflect the quality of regional vegetation; Climate indicators are designed, and the accumulated temperature from April to June and the day-night temperature difference from June to September correspond to the optimal growth temperature of vegetation and the accumulation level of vegetation organic matter in turn; the accumulated precipitation from April to June and the average precipitation from July to August are related to the germination of vegetation during the greening period and the development of plants during the growth period in turn.

6. The method for arid area vegetation response to climate change according to claim 1 or 3, characterized in that: The vegetation coverage data uses the MOD13A1 remote sensing image of the NASA database, the leaf area index uses the MCD15A3H remote sensing image of the NASA database, the gross primary productivity uses the MOD17A2H remote sensing image of the NASA database, the temperature data uses the MOD11A1 data product of the NASA database, and the precipitation data uses the CHIRPS precipitation product.

7. A device for responding vegetation in arid areas to climate change, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to call the machine-readable program to implement the method according to any one of claims 1 to 6.

8. A computer readable medium, characterized in that The computer readable medium stores computer instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 6.

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