Multi-time-scale vegetation water shortage degree automatic evaluation system and method

Abstract

The invention discloses a multi-time-scale vegetation water shortage degree automatic evaluation system and method, and belongs to the field of global change ecology research. The evaluation method comprises the following steps: collecting remote sensing monitoring data by using a data collection module, and inputting multi-time scale vegetation water shortage degree related data into a data storage module for storage, a rasterization sub-module, a re-sampling sub-module and a mask extraction sub-module in the data processing module are called to carry out rasterization, re-sampling and mask extraction processing on the collected and stored data, and then according to the data output by the data processing module, the mask extraction is carried out on the collected and stored data; and calling a multi-scale standardized rainfall vegetation water demand index (SPVI) calculation sub-module and a multi-time scale vegetation water shortage degree division sub-module in the analysis and evaluation module, and finally outputting a multi-time scale vegetation water shortage degree grade to a display module to realize automatic evaluation. The method is suitable for different site conditions, automatic collection and processing can be achieved, and accurate evaluation of the vegetation water shortage degree of multi-time-scale data is achieved.

Description

Technical Field

[0001] The invention belongs to the field of global change ecology, and specifically provides a multi-time scale vegetation water shortage degree automatic assessment system and method. Background Art

[0002] In the past, due to the lack of meteorological monitoring data, large-scale research could not be carried out. With the development of remote sensing technology, remote sensing monitoring data currently provides a channel for quickly and easily evaluating the dynamic changes of vegetation water shortage and predicting the dynamics of vegetation development over a large area. Since the growth and development of vegetation has a certain lag time and cumulative effect in response to water, when water stress reaches a critical point, vegetation may undergo irreversible degradation or death at some point in the future. However, it is difficult to detect the lag time and cumulative effect, as well as the effect of the duration of water stress on vegetation growth and development, using a single time scale. Therefore, evaluating the degree of vegetation water shortage at multiple time scales is of great scientific significance for in-depth understanding of the mechanism of the influence of time scale on vegetation growth and development, and accurately predicting the dynamic changes of vegetation under future climate change. However, the existing vegetation water shortage related indices (mainly precipitation anomaly percentage, standardized precipitation index, standardized precipitation evapotranspiration index, vegetation ecological water demand, etc.) cannot simultaneously achieve rapid evaluation of vegetation water shortage at multiple time scales in large non-uniform areas. Among them, the precipitation anomaly percentage and the standardized precipitation index only involve precipitation, and do not consider the impact of temperature on vegetation evapotranspiration, which has certain limitations; the calculation process of the standardized precipitation evapotranspiration index is too dependent on precipitation and evapotranspiration, and lacks the correction of vegetation and soil coefficients under different site conditions; the traditional determination of vegetation ecological water demand requires obtaining vegetation type and density through field experiments, using lysimeters and TDP to determine the evapotranspiration of individual vegetation and field water holding capacity, etc., which has problems such as long measurement cycle and large workload of data collection. The determination of vegetation ecological water demand based on remote sensing technology is simple and fast, but it can only be carried out based on a certain time scale, and it is impossible to characterize the degree of vegetation water demand at multiple time scales. In summary, there is an urgent need for a vegetation water shortage assessment system and method that is suitable for different site conditions and can automatically collect and process multi-time scale data. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention aims to provide a method for automatically evaluating the degree of vegetation water shortage in a certain area at multiple time scales, in which the required parameters can be obtained in a non-contact manner with the vegetation, the calculation method is simple and fast, and the cost is low, thereby laying a foundation for future research on large-scale calculation of vegetation resilience and resistance and evaluation of ecosystem stability.

[0004] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0005] A multi-time scale vegetation water shortage degree automatic assessment method comprises the following steps:

[0006] 1) The data collection module obtains 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 pre-processes the remote sensing monitoring data;

[0009] 4) The analysis and evaluation module analyzes the pre-processed 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] The step 3) comprises the following steps:

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

[0013] 3.2) The resampling submodule resamples the data after the unified form so that the number of grid rows and columns of the data 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 precipitation data, actual evapotranspiration data, soil type data and leaf area index data in the resampled data.

[0015] The step 4) comprises the following steps:

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

[0017] 4.2) Multi-time scale vegetation water shortage degree classification submodule divides the multi-time scale vegetation water shortage level according to SPVI.

[0018] The step 4.1) comprises the following steps:

[0019] 4.1.1) Obtain vegetation water deficit conditions D based on the data of the assessment object i :

[0020] D i =P i -ET i

[0021] Among them, P i is the accumulated precipitation in period i, ET i is the ecological water demand of vegetation in period i;

[0022] 4.1.2) Establishing water deficit accumulation series at different time scales

[0023]

[0024] Among them, k is the time scale and n is the number of calculations;

[0025] 4.1.3) The three-parameter log-logistic probability distribution function is used to fit the water deficit accumulation series at different time scales, and the three-parameter log-logistic probability distribution function F(x) is obtained:

[0026]

[0027] Among them, 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;

[0028] 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:

[0029] SPVI=W-(C0+C1W+C2W 2 ) / (1+d1W+d2W 2 +d3W 3 )

[0030]

[0031] Among them, when F(x)≤0.5, P=1-F(x); when F(x)>0.5, P=1-P, C0, C1, C2, d1, d2 and d3 are all constant terms.

[0032] The vegetation ecological water requirement ET q for:

[0033] ET q =ET0×K c ×K s ×S×10000

[0034] Among them, S is the vegetation area, ET0 is the actual evapotranspiration, K c is the vegetation coefficient, K s is the soil moisture coefficient.

[0035] The vegetation coefficient K c The fitting method is:

[0036] K c =0.428LAI 0.6988

[0037] Among them, LAI is leaf area index.

[0038] The soil moisture coefficient K s for:

[0039] K s =ln[(SS w ) / (S c -S w )×100+1] / ln 101

[0040] Among them, S is the actual soil moisture content, S w is the soil wilting water content, S c is the critical moisture content of soil.

[0041] An automatic assessment system for vegetation water shortage at multiple time scales, including:

[0042] A data collection module is used to obtain remote sensing monitoring data related to the degree of vegetation water shortage in the survey and assessment area through data integration methods;

[0043] A data storage module is used to define and store remote sensing monitoring data;

[0044] A data processing module is used to pre-process remote sensing monitoring data;

[0045] The analysis and evaluation module is used to analyze the pre-processed remote sensing monitoring data to obtain the vegetation water shortage level at multiple time scales;

[0046] The display module is used to display the vegetation water shortage levels at multiple time scales.

[0047] The data processing module comprises:

[0048] The rasterization submodule is used to unify the form of remote sensing monitoring data into raster data form;

[0049] The resampling submodule is used to resample the data after the unified form so that the number of grid rows and columns of the data is the same;

[0050] The mask extraction submodule is used to extract the evaluation object data from the precipitation data, actual evapotranspiration data, soil type data, and leaf area index data in the resampled data using the boundary vector of the study area as a mask.

[0051] The analysis and evaluation module includes:

[0052] The SPVI calculation submodule is used to calculate the multi-time scale standardized precipitation vegetation water index SPVI using the extracted assessment object data;

[0053] The multi-time scale vegetation water shortage degree classification submodule is used to classify the multi-time scale vegetation water shortage level according to SPVI.

[0054] The present invention has the following beneficial effects and advantages:

[0055] The present invention is applicable to different site conditions, can automatically collect and process data, and realize accurate assessment of the degree of vegetation water shortage of multi-time scale data, which is used to guide regional vegetation management and ecological restoration work, and lays a foundation for research on large-scale calculation of vegetation resilience and resistance and evaluation of ecosystem stability. At the same time, the multi-scale standardized precipitation vegetation water requirement index SPVI established by the present invention provides a scientific reference for exploring the hysteresis and cumulative effects of vegetation growth and development in response to water and for in-depth understanding of the mechanism of the influence of time scale on vegetation growth and development, and has great promotion and application value in the fields of global change ecology and ecological restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a framework diagram of the multi-time scale vegetation water shortage degree automatic assessment system of the present invention;

[0057] Figure 2 It is a flow chart of the multi-time scale vegetation water shortage degree automatic assessment method of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0059] Combination Figure 1 As shown, the multi-time scale vegetation water shortage degree automatic assessment system of the present invention is composed 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 by means of data integration and outputs it to the data storage module; the data storage module is used to define and store the multi-time scale vegetation water shortage degree related data and output it to the data processing module; the data processing module sets three sub-data processing modules, namely, a rasterization module, a resampling module, and a mask extraction module, according to the processing steps, for processing the multi-time scale vegetation water shortage degree related data and outputting it to the analysis and evaluation module data processing module; the analysis and evaluation module sets a multi-scale standardized precipitation vegetation water demand index (SPVI) calculation sub-module and a multi-time scale vegetation water shortage degree division sub-module according to the analysis and evaluation steps, for calculating the multi-time scale standardized precipitation vegetation water demand index (SPVI) and dividing the multi-time scale vegetation water shortage level and outputting it to the display module; the display module is used to display the multi-time scale vegetation water shortage level.

[0060] The multi-time scale vegetation water shortage degree automatic evaluation method of the present invention uses the multi-time scale vegetation water shortage degree automatic evaluation system of the present invention to evaluate the vegetation water shortage degree. Figure 2 As shown, the method first determines the assessment area, and through the investigation and analysis of the basic conditions of vegetation, selects a certain vegetation type or vegetation within a certain range as the assessment object according to the assessment requirements. Through the data collection module, the data related to the degree of vegetation water shortage in the investigation and assessment area are collected, including the boundary vector data of the study area, precipitation data, actual evapotranspiration data, soil type data, soil moisture data, and leaf area index data. The collected data related to the degree of vegetation water shortage are stored in the data storage module, so as to perform multi-scale automatic assessment of the degree of vegetation water shortage based on these data.

[0061] In an embodiment of the present invention, data processing includes three steps: rasterization, resampling and mask extraction. The data related to the degree of vegetation water shortage at multiple time scales are unified into the form of raster data, the data is resampled to make the data resolution uniform and the number of raster rows and columns the same, and the data of the research object is extracted using the boundary vector of the study area as a mask.

[0062] After the preliminary work is completed, the multi-time scale standardized precipitation vegetation water requirement index SPVI is constructed according to the principle of vegetation water deficit, and the constructed time scale standardized precipitation vegetation water requirement index is placed in the analysis and evaluation module of the multi-time scale vegetation water shortage degree automatic evaluation system, making the evaluation of vegetation water shortage degree more scientific. The following is a detailed description of the process of multi-time scale vegetation water shortage degree automatic analysis and evaluation combined with the specific implementation process.

[0063] 1. Since vegetation water deficit is caused by the imbalance between water supply and demand, the water supply of vegetation is mainly precipitation. Water demand can be expressed by ecological water demand. The difference between precipitation and vegetation ecological water demand is D i The water deficit of vegetation is:

[0064] D i =P i -ET i

[0065] Where: P i is the accumulated precipitation in period i, ET i is the ecological water requirement of vegetation in period i, which is mainly related to climate, soil moisture content, vegetation and soil type. The calculation method is as follows:

[0066] ET q =ET0×K c ×K s ×S×10000

[0067] Where S is the vegetation area; ET0 is the actual evapotranspiration; K c is the vegetation coefficient; K s is the soil moisture coefficient. c It is highly correlated with the leaf area index, so the leaf area index is used to fit the vegetation coefficient. The fitting model is as follows:

[0068] K c =0.428LAI 0.6988

[0069] K s It is related to soil type, soil texture and soil water content. The calculation formula is as follows:

[0070] K s =ln[(SS w ) / (S c -S w )×100+1] / ln 101

[0071] Where S is the actual soil moisture content; S w S is the soil wilting water content; c The soil types in the study area were statistically analyzed, and 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 Chinese Soil Database. The data were input into the Soil Water Characteristics module in the SPAW soil calculation software to calculate the soil wilting water content and soil critical water content.

[0072] 2. Based on the vegetation water deficit calculated in step 1, establish the water deficit accumulation series at different time scales:

[0073]

[0074] Where: k is the time scale, k = 1, 2, 3…48, and n is the number of calculations.

[0075] 3. For D n k Fitting is done using a three-parameter log-logistic probability density function:

[0076]

[0077] Where: 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.

[0078] 4. Perform standard normal distribution transformation on the probability sequence to obtain the corresponding SPVI sequence:

[0079] SPVI=W-(C0+C1W+C2W 2 ) / (1+d1W+d2W 2 +d3W 3 )

[0080]

[0081] In the formula: F( x ) ≤0.5, P=1-F(x); when F( x ) When >0.5, P = 1-P. Constant terms C0 = 2.515517, C1 = 0.802853, C2 = 0.010328, d1 = 1.432788, d2 = 0.189269, d3 = 0.001308.

[0082] 5. Classification of water shortage degree: Since the calculation principle of the standardized precipitation vegetation water requirement index and the standardized precipitation evapotranspiration index is the same, the vegetation water shortage is divided into four levels: 0, 1, 2, and 3, corresponding to no water shortage, mild water shortage, moderate water shortage, and severe water shortage. The classification standards are as follows:

[0083]

[0084]

[0085] 6. Using the multi-time scale vegetation water shortage assessment grade classification results as input data, call the display module to complete the multi-time scale vegetation water shortage assessment grade display.

[0086] Through actual deployment and application in multiple regions, the system runs well, various meteorological, vegetation, soil and other data are fully integrated, and the value of the data is fully mined; the multi-time scale vegetation water shortage degree automatic assessment method of the present invention is verified and utilized, and 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 actual operation.

[0087] The present invention solves the defects of existing vegetation water shortage related indexes (precipitation anomaly percentage, standardized precipitation index, standardized precipitation evapotranspiration index, vegetation ecological water demand), such as not considering the influence of temperature on vegetation evapotranspiration, over-reliance on precipitation and evapotranspiration, lack of correction of vegetation and soil coefficients, large workload of data collection, and inability to characterize the degree of vegetation water demand at multiple time scales. The present invention is applicable to different site conditions, can automatically collect and process, and realizes accurate assessment of the degree of vegetation water shortage of multi-time scale data, which is used to guide regional vegetation management and ecological restoration work, and lays a foundation for related research such as large-scale calculation of vegetation resilience and resistance and evaluation of ecosystem stability. At the same time, the multi-scale standardized precipitation vegetation water demand index SPVI established by the present invention provides scientific reference for exploring the hysteresis and cumulative effects of vegetation growth and development response to water and in-depth understanding of the mechanism of the influence of time scale on vegetation growth and development, and has great promotion and application value in the fields of global change ecology and ecological restoration.

Claims

1. A multi-time scale vegetation water shortage degree automatic assessment method, characterized in that: The following steps are involved: 1) The data collection module obtains 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 pre-processes the remote sensing monitoring data; 4) The analysis and evaluation module analyzes the pre-processed 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 method for automatically assessing the degree of vegetation water shortage at multiple time scales according to claim 1, characterized in that: The step 3) comprises the following steps: 3.1) The rasterization submodule unifies the remote sensing monitoring data into raster data form; 3.2) The resampling submodule resamples the data after the unified form so that the number of grid rows and columns of the data 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 precipitation data, actual evapotranspiration data, soil type data and leaf area index data in the resampled data.

3. The method for automatically assessing the degree of vegetation water shortage at multiple time scales according to claim 1 is characterized in that: The step 4) comprises the following steps: 4.1) The SPVI calculation submodule uses the extracted assessment object data to calculate the multi-time scale standardized precipitation vegetation water index SPVI; 4.2) Multi-time scale vegetation water shortage degree classification submodule divides the multi-time scale vegetation water shortage level according to SPVI.

4. The method for automatically assessing the degree of vegetation water shortage at multiple time scales according to claim 3, wherein step 4.1) comprises the following steps: 4.1.1) Obtain vegetation water deficit conditions D based on the data of the assessment object i : D i =P i -AND i in, P i is the accumulated precipitation in period i, ET i is the ecological water demand of vegetation in period i; 4.1.2) Establishing water deficit accumulation series at different time scales Among them, k is the time scale and n is the number of calculations; 4.1.3) The three-parameter log-logistic probability distribution function is used to fit the water deficit accumulation series at different time scales, and the three-parameter log-logistic probability distribution function F(x) is obtained: Among them, 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 ) Among them, when F(x)≤0.5, P=1-F(x); when F(x)>0.5, P=1-P, C0, C1, C2, d1, d2 and d3 are all constant terms.

5. The method for automatically assessing the degree of vegetation water shortage at multiple time scales according to claim 4 is characterized in that: The vegetation ecological water requirement ET q for: AND q =ET0×K c ×K s ×S×10000 Among them, S is the vegetation area, ET0 is the actual evapotranspiration, K c is the vegetation coefficient, K s is the soil moisture coefficient.

6. The method for automatically assessing the degree of 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 Among them, LAI is leaf area index.

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

8. A multi-time scale vegetation water shortage degree automatic assessment system, characterized by: include: A data collection module is used to obtain remote sensing monitoring data related to the degree of vegetation water shortage in the survey and assessment area through data integration methods; A data storage module is used to define and store remote sensing monitoring data; A data processing module is used to pre-process remote sensing monitoring data; The analysis and evaluation module is used to analyze the pre-processed 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 levels at multiple time scales.

9. The multi-time scale vegetation water shortage automatic assessment system according to claim 8, characterized in that: The data processing module comprises: The rasterization submodule is used to unify the form of remote sensing monitoring data into raster data form; The resampling submodule is used to resample the data after the unified form so that the number of grid rows and columns of the data is the same; The mask extraction submodule is used to extract the evaluation object data from the precipitation data, actual evapotranspiration data, soil type data, and leaf area index data in the resampled data using the boundary vector of the study area as a mask.

10. The multi-time scale vegetation water shortage automatic assessment system according to claim 8, characterized in that: The analysis and evaluation module includes: The SPVI calculation submodule is used to calculate the multi-time scale standardized precipitation vegetation water index SPVI using the extracted assessment object data; The multi-time scale vegetation water shortage degree classification submodule is used to classify the multi-time scale vegetation water shortage level according to SPVI.

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