Quantitative prediction method for vegetation recovery duration under influence of drought event

Abstract

The invention discloses a quantitative prediction method for vegetation recovery duration under the influence of a drought event. The method comprises the following steps: extracting a time sequence of a vegetation state index and a soil water content of each grid point weekly scale in a research area; according to the vegetation state index time sequence, obtaining a lattice-by-lattice vegetation abnormal condition; identifying a soil drought event of the research area based on a soil moisture digit method, and extracting lattice-by-lattice drought typical features; in combination with the drought event and the corresponding vegetation abnormal condition, a vegetation accumulation horizontal curve is drawn, and the vegetation recovery duration is quantified by identifying the interval between the starting moment and the ending moment of vegetation recovery; and introducing an extreme gradient lifting tree model, and training and predicting the vegetation recovery duration after drought occurs at a future moment. According to the method, a new way is provided for quantifying the vegetation recovery duration, the method for predicting the vegetation recovery duration under the drought influence is established based on the machine learning algorithm, and technical support is provided for evaluating the response of an ecological system to drought.

Description

Technical Field

[0001] The invention relates to a method for estimating the duration of vegetation recovery, and in particular to a method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event. Background Art

[0002] Drought is a natural disaster event that occurs frequently and lasts for a long time. It affects the hydrological cycle, increases the risk of forest dieback, causes land degradation, and even reduces the biodiversity of terrestrial ecosystems, which has serious negative effects on the ecological environment, social economy, and especially agricultural production. At the same time, global warming has increased the frequency and intensity of droughts, and the risk of vegetation ecosystems suffering from drought is also increasing. In this context, how vegetation responds to and adapts to the increasingly severe drought environment is one of the difficult issues in the current research in the fields of drought and ecology at home and abroad.

[0003] Vegetation index is an important parameter to characterize the growth status and dynamic changes of vegetation. For example, the normalized difference vegetation index ( ), Enhanced Vegetation Index ( ), leaf area index ( ) and so on. These indices can not only monitor the long-term evolution characteristics of vegetation, but also reflect the recovery of vegetation after natural disasters such as drought and high temperature. Previous studies have analyzed the response of vegetation to drought based on vegetation indices, especially focusing on the time required for vegetation to recover from an abnormal state to a normal state after a drought occurs, that is, the recovery time. Vegetation recovery time is an important indicator for assessing the resilience of ecosystems, especially in areas where drought events occur frequently. Accurately predicting vegetation recovery time is of great significance for ecological restoration and resource management.

[0004] However, most studies determine the start and end time of vegetation recovery under drought based on the instantaneous anomalies of vegetation indices. However, they rarely consider the cumulative effects of drought on vegetation, and rarely analyze the response of vegetation to drought events from the perspective of cumulative vegetation loss. In addition, limited research is based on machine learning models, and there is a lack of models with the expected accuracy to simulate and predict the duration of vegetation recovery. Summary of the invention

[0005] Purpose of the invention: In view of the shortcomings of existing research, the present invention proposes a quantitative prediction method for the duration of vegetation recovery under the influence of drought events, which can obtain the prediction results of the duration of vegetation recovery under the influence of drought events and the accuracy meets the requirements of disaster warning.

[0006] Technical solution: The technical solution adopted by the present invention is a quantitative prediction method for the duration of vegetation recovery under the influence of drought events, comprising the following steps: Step 1: Extract the vegetation status index at the grid point level in the study area and soil moisture content time series; Step 2: After de-seasoning and de-trending the time series of the vegetation status index at each grid point, the anomaly value is obtained, and the abnormal vegetation condition corresponding to each grid point is identified according to the anomaly value; For the time series of soil moisture content at each grid point, the soil drought events corresponding to each grid point are identified based on the soil moisture median method, and the typical drought characteristics of each grid point are extracted; Step 3: Combine the soil drought events at each grid point with the corresponding abnormal vegetation conditions to obtain the vegetation cumulative anomaly curve. Use the vegetation cumulative anomaly curve to identify the start and end time of the abnormal vegetation state recovery and calculate the vegetation recovery time. ; Step 4: Based on the typical characteristics of drought events at each grid point in the study area, canopy height , root depth , soil texture and groundwater depth Construct an independent variable sequence, based on the duration of vegetation recovery As the dependent variable sequence, the extreme gradient boosting tree model was trained, and the vegetation recovery time of the study area was simulated and predicted through the trained extreme gradient boosting tree model.

[0007] The “deseasonalization” processing includes: using the difference method, the monthly data of the vegetation state index are subtracted from the multi-year monthly average; The “detrending” process includes: fitting the original data of the vegetation state index by the least square method to obtain a fitting curve, and then subtracting the fitting curve of the original time series from the original time series of the vegetation state index; According to the anomaly value, the vegetation anomaly corresponding to each grid point is identified, including: when the anomaly value is less than 0 for the first time, the corresponding time is defined as the start time of the vegetation negative anomaly event; when the anomaly value returns to not less than 0 again, the corresponding time is defined as the end time of the vegetation negative anomaly event; between the start time and the end time, the minimum value of the anomaly value is defined as the peak value of the vegetation negative anomaly event; For the time series of soil moisture content at each grid point, identifying the soil drought events corresponding to each grid point based on the soil moisture median method includes: splitting the time series of soil moisture content in each year into several time periods, and combining the corresponding time periods of several years; selecting the most suitable distribution function from a variety of cumulative probability distribution functions, and converting the combined data into a soil moisture median sequence; identifying soil drought events according to the soil moisture median sequence; the cumulative probability distribution functions include gamma distribution, beta distribution, extreme value distribution, generalized extreme value distribution, logarithmic distribution, logarithmic distribution and Weibull distribution.

[0008] The typical characteristics of drought include drought duration, drought intensity and drought peak; drought duration refers to the duration from when the soil moisture content decreases below a first threshold to when it recovers or exceeds the first threshold; drought intensity refers to the average value of the soil moisture content during the drought; and drought peak refers to the minimum value of the soil moisture content during the drought.

[0009] In response to drought events For vegetation negative anomaly events that occur after one week, the vegetation anomaly values ​​at each moment of the vegetation negative anomaly event are accumulated moment by moment to obtain the vegetation cumulative anomaly curve.

[0010] The vegetation cumulative anomaly curve is used to identify the start and end time of vegetation abnormal state recovery and calculate the vegetation recovery time. Including: The moment when the vegetation cumulative anomaly reaches the local minimum is defined as the moment when the abnormal state of vegetation is restored , the vegetation accumulation anomaly recovers or exceeds 0 and lasts for at least m Weekly, defined as the end time of the abnormal vegetation state recovery , calculate the vegetation recovery time, .

[0011] The trained extreme gradient boosting tree model is used to simulate and predict the vegetation recovery time in the study area. The calculation formula is: , In the formula, is the vegetation recovery time output by the extreme gradient boosting tree model, For the The prediction results of a decision tree, is the total number of trees, The duration of drought, The intensity of drought, The peak of drought, is the canopy height, is the root depth, For soil texture, The depth of groundwater.

[0012] The objective function of the extreme gradient boosting tree model is: , In the formula, is the objective function, Indicates The true value of the samples, Indicates The predicted value of samples, is the number of samples, is the loss function, which measures the model prediction value and the true value differences; For the The prediction results of a decision tree, is the total number of trees, is the regularization term.

[0013] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for quantitatively predicting the duration of vegetation recovery under the influence of drought events is implemented.

[0014] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for quantitatively predicting the duration of vegetation recovery under the influence of drought events is implemented.

[0015] The present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the quantitative prediction method for the duration of vegetation recovery under the influence of a drought event.

[0016] Beneficial effects: Compared with the prior art, the present invention has the following advantages: the recovery time of vegetation under drought disturbance is quantified from the perspective of cumulative vegetation loss, and the cumulative anomaly method can effectively improve the calculation accuracy of vegetation recovery time; the present invention can comprehensively consider various factors such as drought characteristics, vegetation, soil, groundwater, etc., and realize accurate simulation and prediction of vegetation recovery time by introducing the extreme gradient boosting tree (XGBoost) model with efficient feature learning and nonlinear modeling capabilities. This method can capture the complex nonlinear relationship in the vegetation recovery process, and can significantly improve the prediction accuracy compared with linear models or simple statistical methods, providing a scientific basis for the evaluation of the adaptability of vegetation ecosystems to drought and agricultural disaster monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the quantitative prediction method of vegetation recovery time under the influence of drought events described in the present invention; Figure 2 It is a schematic diagram of soil drought identification and typical characteristics according to the present invention; Figure 3 1. (a) The vegetation cumulative anomaly curve and (b) The schematic diagram of vegetation recovery time according to the present invention; Figure 4 It is a framework diagram of the extreme gradient boosting tree model described in the present invention for predicting the duration of future vegetation recovery. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0019] The quantitative prediction method for the vegetation recovery time under the influence of drought events described in the present invention has a flow chart as follows: Figure 1 This embodiment is based on Taking the index as an example, this paper describes the quantitative prediction method of vegetation recovery time under drought disturbance. This method is also applicable to , Other vegetation indices.

[0020] S1: Extract the vegetation status index at the grid point level in the study area and soil moisture content time series.

[0021] S2: After de-seasoning and de-trending the grid-by-grid vegetation state index time series, the corresponding vegetation anomaly conditions are obtained.

[0022] Among them, "deseasonalization" processing refers to removing the influence of seasonal changes from the original vegetation data in order to more accurately analyze the long-term trends and changes of the time series. The "deseasonalization" processing in the present invention adopts the difference method, the principle of which is to subtract the multi-year monthly average from the monthly vegetation data, thereby removing the seasonal influence of the data.

[0023] "Detrending" processing refers to eliminating the components of vegetation data that change over time, which is conducive to accurately analyzing the fluctuations of the data itself. Vegetation data may be affected by long-term climate change or human activities, showing an upward or downward trend. Detrending processing can eliminate these long-term changes and make the data more stable. The "detrending" in the present invention is achieved by subtracting the least squares fitting curve from the original data. The mean of the vegetation data after detrending is zero, which is convenient for statistical analysis of negative abnormal events in vegetation.

[0024] The weekly data obtained after removing the "season" and "trend" of the vegetation data are the anomalies relative to the long-term average. When the anomaly value is less than 0 for the first time, it indicates that the vegetation begins to have negative anomalies, and this moment is defined as the beginning of the vegetation negative anomaly; when the anomaly value returns to 0 or exceeds 0 again, it indicates that the vegetation begins to return to normal, and this moment is defined as the end of the vegetation negative anomaly event. Between these two moments, the minimum value of the vegetation anomaly is defined as the peak value of this vegetation negative anomaly event. By identifying the beginning and end times of the vegetation negative anomaly, the vegetation negative anomaly events can be obtained at each grid point.

[0025] S3: Identify soil drought events in the study area based on the soil moisture fraction method and extract typical drought characteristics at each grid point.

[0026] Among them, the "soil moisture median method" refers to splitting the original soil moisture time series into 52 time series (taking 1940-2022 as an example, each year can be split into 52 weeks, combining the first week of all years, the second week of all years, and so on, the 52nd week of all years) to eliminate the seasonal impact of soil moisture, and then select a suitable distribution function from a variety of candidate cumulative probability distribution functions (such as gamma distribution, beta distribution, extreme value distribution, generalized extreme value distribution, logistic distribution, logarithmic distribution, Weibull distribution, etc.) to convert it into a soil moisture median series.

[0027] “Drought events” refer to agricultural drought events identified based on soil moisture quantiles. The identification of drought events requires the following conditions to be met: (1) For a given drought event, Below the 40th percentile, The first moment below the 40th percentile indicates the beginning of the drought event ( Figure 2 In ), and when When the event recovers to or exceeds the 40th percentile, the end time of the event ( Figure 2 In ) are determined; (2) the drought process must contain at least one moment (3) Given the limited impact of short-term drought events on agricultural production and ecosystems, events with a duration (from the start to the end) of less than 12 weeks (about 3 months) were excluded.

[0028] The “typical characteristics of drought” include drought duration ( ), drought intensity ( )、Drought Peak( ). Schematic diagram of soil drought identification and typical characteristics Figure 2 As shown. The drought duration refers to the duration from the moment when the soil moisture content is lower than the 40th percentile to the moment when it recovers or exceeds the 40th percentile. The drought intensity refers to the average value of the soil moisture content during the drought; the drought peak refers to the minimum value of the soil moisture content during the drought. The specific formulas for drought duration, intensity and peak can be expressed as: , , ,

[0029] In the formula, is the start time of the drought event, is the end time of the drought event; for The soil moisture content at the time; Represents the time series of soil moisture values ​​during drought.

[0030] There is no restriction on the order of steps S2 and S3.

[0031] S4: Combine the drought events at each grid point with the corresponding vegetation anomaly conditions to draw a vegetation cumulative anomaly curve, identify the start and end time of vegetation recovery through the curve, and calculate the duration of vegetation recovery .

[0032] Among them, the “vegetation accumulation anomaly curve” refers to the For vegetation negative anomaly events that occur after one week, the vegetation anomaly values ​​at each moment of the vegetation negative anomaly event are accumulated moment by moment to obtain the vegetation cumulative anomaly curve, as shown in Figure 3 As shown in (a) in the present invention, , that is, the negative abnormal vegetation conditions within 12 weeks (about 3 months) after the drought event are related to this drought event.

[0033] The "vegetation recovery time" mentioned above refers to the time interval from the time when the vegetation has the maximum local cumulative loss to the time when it recovers to the normal state after the drought event occurs. There are two important time nodes involved. The time when the vegetation cumulative anomaly reaches the local minimum after the drought event is defined as the starting time of the abnormal vegetation state recovery. ,like Figure 3 When the vegetation accumulation anomaly gradually increases and recovers again or exceeds 0 and lasts for at least 2 weeks, it is defined as the end time of vegetation abnormal state recovery. (like Figure 3 ), so the vegetation recovery time .

[0034] S5: Introduce the extreme gradient boosting tree model to simulate and predict the vegetation recovery time in the study area.

[0035] Extract the typical characteristics of drought events at each grid point in the historical period of the study area (drought duration , drought intensity , drought peak ), canopy height , root depth , soil texture and groundwater depth Data construction independent variable sequence, vegetation recovery duration As the dependent variable sequence, 80% of the independent variable data are randomly selected as training samples and 20% as test samples.

[0036] Put the training samples into the extreme gradient boosting decision tree (XGBoost) model and set the model's hyperparameters, including learning rate, maximum tree depth, regularization coefficient (lambda), etc. Use the gradient boosting algorithm to gradually optimize the objective function: , In the formula, Indicates The true value of the samples, Indicates The predicted value of samples, is the number of samples, is the loss function, which measures the model prediction value and the true value differences; For the The prediction results of a decision tree, is the total number of trees, is a regularization term that controls the complexity of the model.

[0037] The performance of the model is evaluated using the test sample, and the evaluation indicator is the mean square error. and Nash efficiency coefficient , the former can reflect the overall size of the prediction error, and the latter can reflect the degree of fit between the predicted value and the true value. The formula is as follows: , , In the formula, Represents the mean of the true values.

[0038] By monitoring the weekly soil moisture changes in the study area, identifying soil drought events, extracting typical characteristics of drought duration, intensity, and peak, and combining the monitoring data of canopy height, root depth, soil texture, and groundwater depth, and substituting them into the trained extreme gradient boosting decision tree model, the vegetation recovery time of any grid point can be predicted. ,like Figure 4 As shown, the calculation formula is: , In the formula, For the The prediction results of a decision tree, is the total number of trees.

[0039] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event is implemented.

[0040] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event is implemented.

[0041] In one embodiment, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event.

[0042] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0043] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

Claims

1. A quantitative prediction method for the duration of vegetation recovery under the influence of drought events, characterized in that: The following steps are involved: Step 1: Extract the vegetation status index at the grid point level in the study area and soil moisture content time series; Step 2: After de-seasoning and de-trending the time series of the vegetation status index at each grid point, the anomaly value is obtained, and the abnormal vegetation condition corresponding to each grid point is identified according to the anomaly value; For the time series of soil moisture content at each grid point, the soil drought events corresponding to each grid point are identified based on the soil moisture median method, and the typical drought characteristics of each grid point are extracted; Step 3: Combine the soil drought events at each grid point with the corresponding abnormal vegetation conditions to obtain the vegetation cumulative anomaly curve. Use the vegetation cumulative anomaly curve to identify the start and end time of the abnormal vegetation state recovery and calculate the vegetation recovery time. ; Step 4: Based on the typical characteristics of drought events at each grid point in the study area, canopy height , root depth , soil texture and groundwater depth Construct an independent variable sequence, based on the duration of vegetation recovery As the dependent variable sequence, the extreme gradient boosting tree model was trained, and the vegetation recovery time of the study area was simulated and predicted through the trained extreme gradient boosting tree model.

2. The quantitative prediction method for vegetation recovery time under the influence of drought events according to claim 1 is characterized by: The "deseasonalization" processing includes: using the difference method, the monthly data of the vegetation state index are subtracted from the multi-year monthly average; The "detrending" process includes: fitting the original data of the vegetation state index by the least square method to obtain a fitting curve, and then subtracting the fitting curve of the original time series from the original time series of the vegetation state index; According to the anomaly value, the vegetation anomaly corresponding to each grid point is identified, including: when the anomaly value is less than 0 for the first time, the corresponding time is defined as the start time of the vegetation negative anomaly event; when the anomaly value returns to not less than 0 again, the corresponding time is defined as the end time of the vegetation negative anomaly event; between the start time and the end time, the minimum value of the anomaly value is defined as the peak value of the vegetation negative anomaly event; For the time series of soil moisture content at each grid point, identifying the soil drought events corresponding to each grid point based on the soil moisture median method includes: splitting the time series of soil moisture content in each year into several time periods, and combining the corresponding time periods of several years; selecting a distribution function from a variety of cumulative probability distribution functions, and converting the combined data into a soil moisture median sequence; identifying soil drought events according to the soil moisture median sequence; the cumulative probability distribution function includes gamma distribution, beta distribution, extreme value distribution, generalized extreme value distribution, logarithmic distribution, logarithmic distribution and Weibull distribution.

3. The quantitative prediction method for vegetation recovery time under the influence of drought events according to claim 1 is characterized by: The typical characteristics of drought include drought duration, drought intensity and drought peak; drought duration refers to the duration from when the soil moisture content decreases below a first threshold to when it recovers or exceeds the first threshold; drought intensity refers to the average value of the soil moisture content during the drought; and drought peak refers to the minimum value of the soil moisture content during the drought.

4. The quantitative prediction method for vegetation recovery time under the influence of drought events according to claim 1 is characterized by: In response to drought events For vegetation negative anomaly events that occur after one week, the vegetation anomaly values ​​at each moment of the vegetation negative anomaly event are accumulated moment by moment to obtain the vegetation cumulative anomaly curve.

5. The quantitative prediction method for vegetation recovery time under the influence of drought events according to claim 1, characterized in that: The vegetation cumulative anomaly curve is used to identify the start and end time of vegetation abnormal state recovery and calculate the vegetation recovery time. Including: The moment when the vegetation cumulative anomaly reaches the local minimum is defined as the moment when the abnormal state of vegetation is restored , the vegetation accumulation anomaly recovers or exceeds 0 and lasts for at least m Weekly, defined as the end time of the abnormal vegetation state recovery , calculate the vegetation recovery time, .

6. The quantitative prediction method for vegetation recovery time under the influence of drought events according to claim 1, characterized in that: The trained extreme gradient boosting tree model is used to simulate and predict the vegetation recovery time in the study area. The calculation formula is: ， In the formula, is the vegetation recovery time output by the extreme gradient boosting tree model, For the The prediction results of a decision tree, is the total number of trees, The duration of drought, The intensity of drought, The peak of drought, is the canopy height, is the root depth, For soil texture, The depth of groundwater.

7. The quantitative prediction method for vegetation recovery time under the influence of drought events according to claim 1, characterized in that: The objective function of the extreme gradient boosting tree model is: ， In the formula, is the objective function, Indicates The true value of the samples, Indicates The predicted value of samples, is the number of samples, is the loss function, which measures the model prediction value and the true value differences; For the The prediction results of a decision tree, is the total number of trees, is the regularization term.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or instruction is executed by a processor, the method for quantitatively predicting the duration of vegetation recovery under the influence of a drought event as described in any one of claims 1 to 7 is implemented.

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