Automated assessment system and method for multi-timescale vegetation water deficit levels

Abstract

The present invention belongs to the field of global change ecology research. Disclosed are an automated assessment system and method for multi-timescale vegetation water deficit levels. The assessment method comprises: using a data collection module to collect remote sensing monitoring data; inputting data, which is related to multi-timescale vegetation water deficit levels, into a data storage module for storage; invoking a rasterization sub-module, a resampling sub-module and a mask extraction sub-module in a data processing module to perform rasterization, resampling and mask extraction processing on the collected and stored data; then, on the basis of data outputted by the data processing module, invoking a multi-scale standardized precipitation vegetation water demand index (SPVI) calculation sub-module and a multi-timescale vegetation water deficit level grading sub-module in an analysis and assessment module; and finally, outputting multi-timescale vegetation water deficit levels to a display module, so as to achieve automated assessment. The present invention is applicable to different site conditions, enables automated collection and processing, and achieves precise assessment of vegetation water deficit levels based on multi-timescale data.

Description

An automated assessment system and method for vegetation water shortage at multiple time scales Technical Field

[0001] This invention belongs to the field of global change ecology, specifically providing an automated assessment system and method for vegetation water shortage at multiple time scales. Background Technology

[0002] Previously, insufficient meteorological monitoring data prevented large-scale studies. However, with the development of remote sensing technology, remote sensing data now provides a convenient and rapid way to assess the dynamic changes in vegetation water shortage over large areas and predict vegetation development dynamics. Because vegetation growth and development respond to water with a certain lag time and cumulative effect, when water stress reaches a critical point, vegetation may undergo irreversible degradation or death at some point in the future. A single time scale is insufficient to detect the lag time and cumulative effect, as well as the impact of the duration of water stress on vegetation growth and development. Therefore, assessing vegetation water shortage at multiple time scales is of significant scientific importance for a deeper understanding of the mechanisms by which time scales affect vegetation growth and development and for accurately predicting the dynamic changes in vegetation under future climate change. However, existing vegetation water shortage indices (mainly precipitation anomaly percentage, standardized precipitation index, standardized precipitation evapotranspiration index, and vegetation ecological water requirement) cannot simultaneously and rapidly assess vegetation water shortage at multiple time scales over large, non-uniform areas. Among these methods, the percentage of precipitation anomaly and the standardized precipitation index only involve precipitation and do not consider the impact of temperature on vegetation evapotranspiration, thus having certain limitations. The calculation process of the standardized precipitation evapotranspiration index relies too heavily on precipitation and evapotranspiration, lacking correction for vegetation and soil coefficients under different site conditions. Traditional vegetation ecological water requirement measurement requires obtaining vegetation type and density through field experiments, and using lysimeters and TDP to measure evapotranspiration and field holding capacity of individual plants, which suffers from long measurement cycles and a large workload for data collection. While vegetation ecological water requirement measurement based on remote sensing technology is simple and quick, it can only be carried out based on a single time scale and cannot characterize the degree of vegetation water requirement at multiple time scales. In summary, there is an urgent need for a vegetation water shortage assessment system and method that is applicable to different site conditions and can automatically collect and process data at multiple time scales. Summary of the Invention

[0003] To address the aforementioned shortcomings in existing technologies, this invention aims to provide an automated method for assessing the water shortage level of vegetation at multiple time scales in a given area. This method allows the required parameters to be obtained in a non-contact manner with vegetation, is simple and quick to calculate, and is low in cost. It lays the foundation for future research on large-area calculations of vegetation resilience and resistance, as well as ecosystem stability assessment.

[0004] The technical solution adopted by the present invention to achieve the above objectives is as follows:

[0005] An automated method for assessing vegetation water shortage at multiple time scales includes the following steps:

[0006] 1) The data collection module acquires remote sensing monitoring data related to the degree of vegetation water shortage in the survey and assessment area through data integration methods;

[0007] 2) The data storage module defines and stores remote sensing monitoring data;

[0008] 3) The data processing module preprocesses the remote sensing monitoring data;

[0009] 4) The analysis and evaluation module analyzes the preprocessed remote sensing monitoring data to obtain the vegetation water shortage level at multiple time scales;

[0010] 5) The display module displays the vegetation water shortage level at multiple time scales.

[0011] Step 3) includes the following steps:

[0012] 3.1) The rasterization submodule unifies the format of remote sensing monitoring data into raster data format;

[0013] 3.2) The resampling submodule resamples the data after it has been standardized to ensure that the number of raster rows and columns is the same.

[0014] 3.3) The mask extraction submodule uses the boundary vector of the study area as a mask to extract the evaluation object data from the resampled data, including precipitation data, actual evapotranspiration data, soil type data, and leaf area index data.

[0015] Step 4) includes the following steps:

[0016] 4.1) The SPVI calculation submodule uses the extracted assessment object data to calculate the multi-timescale standardized precipitation-vegetation water demand index (SPVI);

[0017] 4.2) The sub-module for classifying vegetation water shortage at multiple time scales classifies vegetation water shortage levels based on SPVI.

[0018] Step 4.1) includes the following steps:

[0019] 4.1.1) Based on the data of the assessment object, obtain the vegetation water deficit status D. i : D i =P i -ET i

[0020] Among them, P i ET represents the cumulative precipitation for time period i. i Let i be the vegetation ecological water demand during time period i;

[0021] 4.1.2) Establish water deficit accumulation sequences at different time scales.

[0022] Where k is the time scale and n is the number of calculations;

[0023] 4.1.3) The cumulative water deficit series at different time scales were fitted using a three-parameter log-logistic probability distribution function to obtain the three-parameter log-logistic probability distribution function F(x):

[0024] Where α is the scale parameter, β is the shape parameter, γ is the origin parameter, and x is the independent variable of the three-parameter Log-logistic probability density function;

[0025] 4.1.4) Perform a standard normal distribution transformation on the three-parameter log-logistic probability distribution function F(x) to obtain the corresponding SPVI sequence: SPVI = W - (C0 + C1W + C2W) 2 ) / (1+d1W+d2W 2 +d3W 3 )

[0026] Wherein, when F(x)≤0.5, P=1-F(x); when F(x)>0.5, P=1-P, and C0, C1, C2, d1, d2 and d3 are all constant terms.

[0027] The vegetation ecological water requirement ET q For: ET q =ET0×K c ×K s ×S×10000

[0028] Where S is the vegetation area, ET0 is the actual evapotranspiration, and K c K represents the vegetation coefficient. s This is the soil moisture coefficient.

[0029] The vegetation coefficient K c The fitting method is: K c =0.428LAI 0.6988

[0030] LAI stands for Leaf Area Index.

[0031] The soil moisture coefficient K s For: K s =ln[(SS w ) / (S c -Sw )×100+1] / ln 101

[0032] Where S is the actual soil moisture content, S w S represents the soil wilting moisture content. c This represents the critical moisture content of the soil.

[0033] An automated assessment system for vegetation water shortage at multiple time scales includes:

[0034] The data collection module is used to acquire remote sensing monitoring data related to the degree of vegetation water shortage in the survey and assessment area through data integration methods;

[0035] The data storage module is used to define and store remote sensing monitoring data;

[0036] The data processing module is used to preprocess remote sensing monitoring data;

[0037] The analysis and evaluation module is used to analyze the preprocessed remote sensing monitoring data to obtain the vegetation water shortage level at multiple time scales.

[0038] The display module is used to display the vegetation water shortage level at multiple time scales.

[0039] The data processing module includes:

[0040] The rasterization submodule is used to unify the format of remote sensing monitoring data into raster data format.

[0041] The resampling submodule is used to resample the data after it has been standardized in form, so that the number of raster rows and columns of the data is the same.

[0042] The mask extraction submodule is used to extract evaluation object data from the resampled data, including precipitation data, actual evapotranspiration data, soil type data, and leaf area index data, using the boundary vector of the study area as a mask.

[0043] The analysis and evaluation module includes:

[0044] The SPVI calculation submodule is used to calculate the multi-timescale standardized precipitation vegetation water demand index (SPVI) using the extracted assessment object data.

[0045] The multi-timescale vegetation water shortage classification submodule is used to classify the vegetation water shortage level at multiple time scales based on SPVI.

[0046] The present invention has the following beneficial effects and advantages:

[0047] This invention is applicable to different site conditions, can automatically collect and process data, and achieves accurate assessment of vegetation water shortage levels across multiple time scales. It can be used to guide regional vegetation management and ecological restoration work, and lays the foundation for research on large-area calculation of vegetation resilience and resistance, as well as ecosystem stability assessment. At the same time, the multi-scale standardized precipitation-vegetation water demand index (SPVI) established by this invention provides a scientific reference for exploring the lag and cumulative effects of vegetation growth and development in response to water, and for a deeper understanding of the impact mechanism of time scales on vegetation growth and development. It has significant application value in global change ecology, ecological restoration and other fields. Attached Figure Description

[0048] Figure 1 is a framework diagram of the automated assessment system for vegetation water shortage at multiple time scales according to the present invention.

[0049] Figure 2 is a flowchart of the automated assessment method for vegetation water shortage at multiple time scales according to the present invention. Detailed Implementation

[0050] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0051] As shown in Figure 1, the automated assessment system for vegetation water shortage at multiple time scales of the present invention consists of a data collection module, a data storage module, a data processing module, an analysis and evaluation module, and a display module. The data collection module collects remote sensing monitoring data through data integration and outputs it to the data storage module. The data storage module defines and stores the data related to vegetation water shortage at multiple time scales and outputs it to the data processing module. The data processing module, according to the processing steps, sets up three sub-data processing modules: a rasterization module, a resampling module, and a mask extraction module, to process the data related to vegetation water shortage at multiple time scales and output it to the analysis and evaluation module. The analysis and evaluation module, according to the analysis and evaluation steps, sets up a multi-scale standardized precipitation vegetation water demand index (SPVI) calculation sub-module and a multi-time scale vegetation water shortage classification sub-module, to calculate the multi-time scale standardized precipitation vegetation water demand index (SPVI) and classify the multi-time scale vegetation water shortage levels and output them to the display module. The display module displays the multi-time scale vegetation water shortage levels.

[0052] The present invention provides an automated assessment method for vegetation water shortage at multiple time scales. This method utilizes the same automated assessment system to evaluate the degree of vegetation water shortage. As shown in Figure 2, the method first determines the assessment area. Through basic vegetation surveys and analyses, a specific vegetation type or a certain range of vegetation is selected as the assessment object based on the assessment requirements. The vegetation within the assessment area is then used as the assessment object. A data collection module collects relevant data on vegetation water shortage within the survey and assessment area, including boundary vector data of the study area, precipitation data, actual evapotranspiration data, soil type data, soil moisture content data, and leaf area index data. The collected data on vegetation water shortage is stored in a data storage module for automated assessment of vegetation water shortage at multiple time scales.

[0053] In embodiments of the present invention, data processing includes three steps: rasterization, resampling, and mask extraction. Data related to vegetation water shortage at multiple time scales are unified into raster data format. The data is resampled to ensure uniform data resolution and the same number of raster rows and columns. The boundary vector of the study area is used as a mask to extract data of the research object.

[0054] After the preliminary work is completed, a multi-timescale standardized precipitation-vegetation water demand index (SPVI) is constructed based on the principle of vegetation water deficit. This SPVI is then incorporated into the analysis and evaluation module of an automated multi-timescale vegetation water deficit assessment system, making the assessment of vegetation water deficit more scientific. The following section details the process of automated analysis and evaluation of multi-timescale vegetation water deficit.

[0055] 1. Since vegetation water deficit is caused by an imbalance between water supply and demand, the main source of vegetation water supply is precipitation, and water demand can be expressed as ecological water demand. The difference between precipitation and vegetation ecological water demand is D. i Water deficit status of vegetation: D i =P i -ET i

[0056] In the formula: P i ET represents the cumulative precipitation for time period i. i Let ET be the vegetation ecological water requirement for time period i, which is mainly related to climate, soil moisture content, vegetation, and soil type. The calculation method is as follows: ET q =ET0×K c ×K s ×S×10000

[0057] In the formula, S is the vegetation area; ET0 is the actual evapotranspiration; K c K represents the vegetation coefficient. s This represents the soil moisture coefficient. Where K...c The vegetation coefficient is highly correlated with leaf area index (LAI), therefore LAI is used to fit the vegetation coefficient. The fitting model is as follows: K c =0.428LAI 0.6988

[0058] K s The formula for calculating K is related to soil type, soil texture, and soil moisture content. s =ln[(SS w ) / (S c -S w )×100+1] / ln 101

[0059] In the formula, S represents the actual soil moisture content; S w Soil wilting moisture content; S c This refers to the critical soil moisture content. Based on the statistical analysis of soil types in the study area, data such as soil name, sand content, clay content, organic matter content, electrical conductivity, gravel content, soil bulk density, silt content, carbonate or lime content, and organic carbon content were extracted from the HWSD China Soil Database. These data were then input into the Soil Water Characteristics module of the SPAW soil calculation software to calculate the soil wilting moisture content and the critical soil moisture content.

[0060] 2. Based on the vegetation water deficit calculated in step 1, establish cumulative water deficit sequences at different time scales:

[0061] In the formula: k is the time scale, k = 1, 2, 3...48, and n is the number of calculations.

[0062] 3. To Fitting was performed using a three-parameter log-logistic probability density function:

[0063] In the formula: α is the scale parameter, β is the shape parameter, and γ is the origin parameter, all of which are obtained by the L-moment parameter estimation method.

[0064] 4. Perform a standard normal distribution transformation on the probability sequence to obtain the corresponding SPVI sequence: SPVI = W - (C0 + C1W + C2W) 2 ) / (1+d1W+d2W 2 +d3W 3 )

[0065] In the formula: when F(x)≤0.5, P=1-F(x); when F(x)>0.5, P=1-P. The constant terms are C0=2.515517, C1=0.802853, C2=0.010328, d1=1.432788, d2=0.189269, d3=0.001308.

[0066] 5. Classification of Water Shortage Degrees: Since the standardized precipitation vegetation water demand index and the standardized precipitation evapotranspiration index are calculated using the same principle, the vegetation water shortage is classified into four levels: 0, 1, 2, and 3, corresponding to no water shortage, slight water shortage, moderate water shortage, and severe water shortage, respectively. The classification criteria are as follows:

[0067] 6. Using the results of the multi-timescale vegetation water shortage assessment as input data, call the display module to complete the display of the multi-timescale vegetation water shortage assessment level.

[0068] Through practical deployment and application in multiple regions, the system has been operating well, and various meteorological, vegetation, and soil data have been fully integrated, allowing for the full exploitation of data value. The automated assessment method for vegetation water shortage at multiple time scales of this invention has been verified and utilized. The established multi-time scale standardized precipitation-vegetation water shortage index accurately and reasonably reflects the current operating status of the system and plays a positive role in practical operation.

[0069] This invention addresses the shortcomings of existing vegetation water shortage indices (precipitation anomaly percentage, standardized precipitation index, standardized precipitation evapotranspiration index, and vegetation ecological water requirement), such as neglecting the influence of temperature on vegetation evapotranspiration, over-reliance on precipitation and evapotranspiration, lack of correction for vegetation and soil coefficients, large data collection workload, and inability to characterize vegetation water demand at multiple time scales. This invention is applicable to different site conditions, enables automated data collection and processing, and achieves accurate assessment of vegetation water shortage at multiple time scales. It can guide regional vegetation management and ecological restoration, laying the foundation for large-scale calculations of vegetation resilience and resistance, and related research on ecosystem stability assessment. Furthermore, the multi-scale standardized precipitation-vegetation water requirement index (SPVI) established by this invention provides a scientific reference for exploring the lag and cumulative effects of vegetation growth and development in response to water, and for a deeper understanding of the mechanisms by which time scales influence vegetation growth and development. It has significant application value in global change ecology and ecological restoration.

Claims

1. An automated method for assessing vegetation water shortage at multiple time scales, characterized in that, Includes the following steps: 1) The data collection module acquires remote sensing monitoring data related to the degree of vegetation water shortage in the survey and assessment area through data integration methods; 2) The data storage module defines and stores remote sensing monitoring data; 3) The data processing module preprocesses the remote sensing monitoring data; 4) The analysis and evaluation module analyzes the preprocessed remote sensing monitoring data to obtain the vegetation water shortage level at multiple time scales; 5) The display module displays the vegetation water shortage level at multiple time scales.

2. The automated assessment method for vegetation water shortage at multiple time scales according to claim 1, characterized in that, Step 3) includes the following steps: 3.1) The rasterization submodule unifies the format of remote sensing monitoring data into raster data format; 3.2) The resampling submodule resamples the data after it has been standardized to ensure that the number of raster rows and columns is the same. 3.3) The mask extraction submodule uses the boundary vector of the study area as a mask to extract the evaluation object data from the resampled data, including precipitation data, actual evapotranspiration data, soil type data, and leaf area index data.

3. The automated assessment method for vegetation water shortage at multiple time scales according to claim 1, characterized in that, Step 4) includes the following steps: 4.1) The SPVI calculation submodule uses the extracted assessment object data to calculate the multi-timescale standardized precipitation-vegetation water demand index (SPVI); 4.2) The sub-module for classifying vegetation water shortage at multiple time scales classifies vegetation water shortage levels based on SPVI.

4. The automated assessment method for vegetation water shortage at multiple time scales according to claim 3, characterized in that step 4.1) includes the following steps: 4.1.1) Based on the data of the assessment object, obtain the vegetation water deficit status D. i : D i *P i -AND i in, P i ET represents the cumulative precipitation for time period i. i Let i be the vegetation ecological water demand during time period i; 4.1.2) Establish water deficit accumulation sequences at different time scales. Where k is the time scale and n is the number of calculations; 4.1.3) The cumulative water deficit series at different time scales were fitted using a three-parameter log-logistic probability distribution function to obtain the three-parameter log-logistic probability distribution function F(x): Where α is the scale parameter, β is the shape parameter, γ is the origin parameter, and x is the independent variable of the three-parameter Log-logistic probability density function; 4.1.4) Perform a standard normal distribution transformation on the three-parameter log-logistic probability distribution function F(x) to obtain the corresponding SPVI sequence: SPVI=W-(C0+C1W+C2W 2 ) / (1+d1W+d2W 2 +d3W 3 ) Wherein, when F(x)≤0.5, P=1-F(x); when F(x)>0.5, P=1-P, and C0, C1, C2, d1, d2 and d3 are all constant terms.

5. The automated assessment method for vegetation water shortage at multiple time scales according to claim 4, characterized in that, The vegetation ecological water requirement ET q For: ET q =ET0×K c ×K s ×S×10000 Where S is the vegetation area, ET0 is the actual evapotranspiration, and K c K represents the vegetation coefficient. s This is the soil moisture coefficient.

6. The automated assessment method for vegetation water shortage at multiple time scales according to claim 5, characterized in that, The vegetation coefficient K c The fitting method is: K c =0.428LAI 0.6988 LAI stands for Leaf Area Index.

7. The automated assessment method for vegetation water shortage at multiple time scales according to claim 5, characterized in that, The soil moisture coefficient K s For: K s =ln[(SS w ) / (S c -S w )×100+1] / ln 101 Where S is the actual soil moisture content, S w S represents the soil wilting moisture content. c This represents the critical moisture content of the soil.

8. An automated assessment system for vegetation water shortage at multiple time scales, characterized in that, include: The data collection module is used to acquire remote sensing monitoring data related to the degree of vegetation water shortage in the survey and assessment area through data integration methods; The data storage module is used to define and store remote sensing monitoring data; The data processing module is used to preprocess remote sensing monitoring data; The analysis and evaluation module is used to analyze the preprocessed remote sensing monitoring data to obtain the vegetation water shortage level at multiple time scales. The display module is used to display the vegetation water shortage level at multiple time scales.

9. The automated assessment system for vegetation water shortage at multiple time scales according to claim 8, characterized in that, The data processing module includes: The rasterization submodule is used to unify the format of remote sensing monitoring data into raster data format. The resampling submodule is used to resample the data after it has been standardized in form, so that the number of raster rows and columns of the data is the same. The mask extraction submodule is used to extract evaluation object data from the resampled data, including precipitation data, actual evapotranspiration data, soil type data, and leaf area index data, using the boundary vector of the study area as a mask.

10. The automated assessment system for vegetation water shortage at multiple time scales according to claim 8, characterized in that, The analysis and evaluation module includes: The SPVI calculation submodule is used to calculate the multi-timescale standardized precipitation vegetation water demand index (SPVI) using the extracted assessment object data. The multi-timescale vegetation water shortage classification submodule is used to classify the vegetation water shortage level at multiple time scales based on SPVI.

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