Method and device for determining ecological threshold of groundwater level in arid area and electronic equipment

Abstract

This invention provides a method, apparatus, and electronic device for determining the ecological threshold of groundwater level in arid areas, relating to the field of eco-hydrological geology. The method simultaneously considers a first ordered Logit model related to vegetation cover level and a second ordered Logit model related to soil salinization level. The resulting ecological threshold range for groundwater level depends on both vegetation cover level and soil salinization level, taking into account their synergistic effect. Furthermore, the soil salinization level relies on remote sensing image data, which has high spatial coverage and is more objective. The use of an ordered Logit model to process the ordered nature of the graded data makes it more scientific, resulting in a groundwater level ecological threshold range with good accuracy and precision.

Description

Technical Field

[0001] This invention relates to the field of eco-hydrological geology, and in particular to a method, apparatus, and electronic device for determining the ecological threshold of groundwater level in arid areas. Background Technology

[0002] Traditional methods for determining the ecological threshold of groundwater level in arid areas first involve conducting manual field soil sampling in the target area to measure soil salinity indicators such as electrical conductivity. Based on experience or standards, the sample points are then classified into different salinization levels (e.g., non-salinized, slightly, moderate, and severely salinized). Next, the salinization level of the ground sampling points is matched with the corresponding groundwater level depth data to form a sample dataset. Then, a regression model is used to obtain the relationship between groundwater level depth data and salinization level, thereby determining the threshold. This threshold is then applied to remote sensing images of the entire area, classifying each pixel to ultimately generate a distribution map of soil salinization.

[0003] The aforementioned traditional technical solutions have the following technical defects:

[0004] Limitations of single indicators: Traditional methods often rely on single indicators such as vegetation cover (NDVI) or soil salinization to determine the threshold, ignoring the synergistic effect of two factors.

[0005] Subjective grading error: The grading of salinization degree relies on manual sampling, which is highly subjective and has low spatial coverage.

[0006] Linear model bias: Ordinary regression models cannot handle the ordered nature of hierarchical data, resulting in insufficient threshold accuracy.

[0007] In summary, traditional methods for determining the ecological threshold of groundwater level in arid areas suffer from technical problems such as strong subjectivity and poor accuracy. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide a method, apparatus and electronic device for determining the ecological threshold of groundwater level in arid areas, so as to alleviate the technical problems of strong subjectivity and poor accuracy of traditional methods for determining the ecological threshold of groundwater level in arid areas.

[0009] In a first aspect, embodiments of the present invention provide a method for determining the ecological threshold of groundwater level in arid areas, comprising:

[0010] Based on the remote sensing image data of the target area, determine the vegetation cover level and soil salinization level of each location in the target area, and group the groundwater level depth data of multiple monitoring points in the target area according to a preset interval to obtain the depth data group of each monitoring point.

[0011] Based on the geographic information system, the burial depth data of each monitoring point is grouped and associated with the vegetation cover level and soil salinization level corresponding to its spatial location to obtain the first modeling dataset of burial depth data group and vegetation cover level of each monitoring point and the second modeling dataset of burial depth data group and soil salinization level of each monitoring point.

[0012] A first ordered Logit model is constructed based on the first modeling dataset, and a second ordered Logit model is constructed based on the second modeling dataset;

[0013] In the first ordered Logit model, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower limit boundary value of the first burial depth data group is determined as the lower limit of ecological degradation.

[0014] In the second ordered Logit model, the last data group at the burial depth that is not significantly correlated with the change in the soil salinization level is determined, and the upper limit boundary value of the last data group at the burial depth is determined as the upper limit of salinization.

[0015] The lower limit of ecological degradation and the upper limit of salinization are used as the ecological threshold range of groundwater level in the target area.

[0016] Furthermore, based on remote sensing image data of the target area, the vegetation cover level and soil salinization level at each location within the target area are determined, including:

[0017] The vegetation cover at each location within the target area is extracted using Landsat satellite remote sensing imagery, and the vegetation cover at each location within the target area is classified to obtain the vegetation cover level at each location within the target area.

[0018] Soil electrical conductivity at each location within the target area is retrieved based on Sentinel-2 satellite remote sensing imagery, and the soil electrical conductivity at each location within the target area is classified to obtain the soil salinization level at each location within the target area.

[0019] Furthermore, based on the geographic information system, the burial depth data of each monitoring point is grouped and associated with the vegetation cover level and soil salinization level corresponding to its spatial location, including:

[0020] A circular buffer zone with a preset radius is generated, centered on each of the aforementioned monitoring points;

[0021] Extract the average vegetation cover and dominant salinity level within each of the circular buffer zones;

[0022] The vegetation coverage level within the circular buffer zone is determined based on the average vegetation coverage.

[0023] The vegetation cover level within the circular buffer zone and the salinization level corresponding to the dominant salinization level are grouped and correlated with the burial depth data of the corresponding monitoring points.

[0024] Furthermore, after constructing a first ordered Logit model based on the first modeling dataset and a second ordered Logit model based on the second modeling dataset, the method further includes:

[0025] The first ordered Logit model and the second ordered Logit model are verified to obtain the first ordered Logit model and the second ordered Logit model that pass the verification.

[0026] In the first ordered Logit model that has passed the verification, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower limit boundary value of the first burial depth data group is determined as the lower limit of ecological degradation.

[0027] In the validated second ordered Logit model, the last data group at the burial depth that is not significantly correlated with the change in soil salinization level is determined, and the upper limit boundary value of the last data group at the burial depth is determined as the upper limit of salinization.

[0028] Furthermore, the lack of significant correlation means that the p-value of the regression coefficient of the corresponding variable of the burial depth data group in the first or second ordered Logit model is greater than 0.1, and the confidence interval of the regression coefficient includes 0.

[0029] Furthermore, the method also includes:

[0030] Obtain real-time groundwater level depth data for the target area;

[0031] Compare the real-time groundwater level depth data with the groundwater level ecological threshold range;

[0032] If the real-time groundwater level depth data exceeds the groundwater level ecological threshold range, a corresponding control command will be generated and output.

[0033] Furthermore, the target area is an arid region.

[0034] Secondly, embodiments of the present invention also provide a device for determining the ecological threshold of groundwater level in arid areas, comprising:

[0035] The determination and grouping unit is used to determine the vegetation cover level and soil salinization level of each location in the target area based on the remote sensing image data of the target area, and to group the groundwater level depth data of multiple monitoring points in the target area according to a preset interval to obtain the depth data group of each monitoring point.

[0036] The association unit is used to associate the burial depth data group of each monitoring point with the vegetation cover level and the soil salinization level corresponding to its spatial location based on the geographic information system, so as to obtain the first modeling dataset of the burial depth data group of each monitoring point and the vegetation cover level and the second modeling dataset of the burial depth data group of each monitoring point and the soil salinization level.

[0037] A building unit is configured to build a first ordered Logit model based on the first modeling dataset and a second ordered Logit model based on the second modeling dataset.

[0038] The first determining unit is used to determine the first burial depth data group that is not significantly correlated with the change in vegetation cover level in the first ordered Logit model, and to determine the lower limit boundary value of the first burial depth data group as the lower limit of ecological degradation.

[0039] The second determining unit is used to determine the last burial depth data group that is not significantly correlated with the change in the soil salinization level in the second ordered Logit model, and to determine the upper limit boundary value of the last burial depth data group as the salinization upper limit.

[0040] The setting unit is used to set the lower limit of ecological degradation and the upper limit of salinization as the ecological threshold range of groundwater level in the target area.

[0041] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0042] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0043] In this embodiment of the invention, a method for determining the ecological threshold of groundwater level in arid areas is provided, comprising: determining the vegetation cover level and soil salinization level at various locations within the target area based on remote sensing image data of the target area; grouping the groundwater level depth data of multiple monitoring points within the target area according to a preset interval to obtain depth data groups for each monitoring point; and associating the depth data group of each monitoring point with the vegetation cover level and soil salinization level corresponding to its spatial location based on a geographic information system to obtain a first modeling dataset of depth data group and vegetation cover level for each monitoring point, and a dataset of depth data group and soil salinization level for each monitoring point. The second modeling dataset is used; a first ordered Logit model is constructed based on the first modeling dataset, and a second ordered Logit model is constructed based on the second modeling dataset; in the first ordered Logit model, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower boundary value of the first burial depth data group is determined as the lower limit of ecological degradation; in the second ordered Logit model, the last burial depth data group that is not significantly correlated with the change in soil salinization level is determined, and the upper boundary value of the last burial depth data group is determined as the upper limit of salinization; the lower limit of ecological degradation and the upper limit of salinization are used as the ecological threshold range of groundwater level in the target area. As described above, the method for determining the ecological threshold of groundwater level in arid areas in this invention simultaneously considers a first ordered Logit model related to vegetation cover level and a second ordered Logit model related to soil salinization level. The resulting ecological threshold range of groundwater level depends on both vegetation cover level and soil salinization level, taking into account their synergistic effect. Furthermore, the soil salinization level depends on remote sensing image data, which has high spatial coverage and is more objective. The use of the ordered Logit model to process the ordered nature of the graded data makes it more scientific. The resulting ecological threshold range of groundwater level has good accuracy and precision, alleviating the technical problems of strong subjectivity and poor accuracy in traditional methods for determining the ecological threshold of groundwater level in arid areas. Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating a method for determining the ecological threshold of groundwater level in arid areas, provided in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of a device for determining the ecological threshold of groundwater level in arid areas, provided in an embodiment of the present invention.

[0047] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Traditional methods for determining the ecological threshold of groundwater level in arid areas are highly subjective and have poor accuracy.

[0050] Based on this, the method for determining the ecological threshold of groundwater level in arid areas in this invention simultaneously considers a first ordered Logit model related to vegetation cover level and a second ordered Logit model related to soil salinization level. The resulting ecological threshold range of groundwater level depends on both vegetation cover level and soil salinization level, taking into account their synergistic effect. Furthermore, the soil salinization level depends on remote sensing image data, which has high spatial coverage and is more objective. The ordered Logit model is used to process the ordered nature of the graded data, making it more scientific. The resulting ecological threshold range of groundwater level has good accuracy and high precision.

[0051] To facilitate understanding of this embodiment, a method for determining the ecological threshold of groundwater level in arid areas, as disclosed in this embodiment of the invention, will first be described in detail.

[0052] Example 1:

[0053] According to an embodiment of the present invention, an embodiment of a method for determining the ecological threshold of groundwater level in arid areas is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0054] Figure 1 This is a flowchart of a method for determining the ecological threshold of groundwater level in arid areas according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0055] Step S102: Determine the vegetation cover level and soil salinization level of each location in the target area based on the remote sensing image data of the target area, and group the groundwater depth data of multiple monitoring points in the target area according to a preset interval to obtain the depth data group of each monitoring point.

[0056] Specifically, the aforementioned multiple monitoring points can be the locations of groundwater level monitoring wells, and the aforementioned preset intervals can be intervals of 0.2m, such as data groups with a burial depth of 1.0m-1.2m, data groups with a burial depth of 1.2m-1.4m, data groups with a burial depth of 1.4m-1.6m, etc. The embodiments of the present invention do not impose specific limitations on the aforementioned preset intervals.

[0057] Step S104: Based on the geographic information system, the burial depth data group of each monitoring point is associated with the vegetation cover level and soil salinization level corresponding to its spatial location to obtain the first modeling dataset of burial depth data group and vegetation cover level of each monitoring point and the second modeling dataset of burial depth data group and soil salinization level of each monitoring point.

[0058] Step S106: Construct a first ordered Logit model based on the first modeling dataset, and construct a second ordered Logit model based on the second modeling dataset;

[0059] Specifically, the Logit transformation uses the ratio of the probability of a particular outcome occurring to the probability of that outcome not occurring as the odds, and the logarithm of this ratio is the logit transformation. The probability of the dependent variable occurring is between 0 and 1. With 0.5 as the symmetric point, the corresponding logit(p) distribution is:

[0060] p=0, logit(p)=In(0 / 1)=-∞;

[0061] p=0.5, logit(p)=In(0.5 / 0.5)=0;

[0062] p=1, logit(p)=In(1 / 0)=+∞.

[0063] After the logit transformation, the range of Logit(p) falls within the interval (-∞, +∞) with 0 as the symmetric point. This makes the prediction of the value of P meaningful regardless of the value of the independent variable.

[0064] Logistic regression models are well-suited for modeling categorical data. However, in practical research, the number of levels (categories) of the dependent variable is not limited to two. When the number of categories is greater than two, a multi-category logistic regression model is needed to fit the independent variables. Furthermore, based on whether there is a rank relationship between the levels of the dependent variable, multi-category logistic regression models are further divided into unordered and ordered multi-category logistic regression models. In this invention, soil salinization (i.e., soil electrical conductivity) and NDVI (i.e., vegetation cover) are both classified and graded; therefore, an ordered logistic model is used to analyze the impact of groundwater depth on vegetation cover grade and soil salinization grade.

[0065] When the categories of a qualitative dependent variable have an rank relationship and the number of categories is greater than 2, this type of data is called multi-category ordinal dependent variable data. When performing logistic regression analysis on this type of data, the number of Logit models to fit is (number of categories minus one). This model is called an ordinal Logit model.

[0066] If the dependent variable (i.e., vegetation cover level or soil salinity level) has 1, 2, ..., j ordered categories, and the independent variable (i.e., burial depth data grouping) has k categories, then j-1 models should be fitted simultaneously:

[0067] Logit1=In[p1 / (1-p1)]=α1+β1x1+…+β k x k ;

[0068] Logit2=In[(p1+p2) / (1-p1-p2)]=α2+β1x1+…+β k x k ;

[0069] …

[0070] Logit j-1 =In[(p1+p2+…+p j-1 ) / (1-p1-p2-…-p j-1 )]=α j-1 +β1x1+…+β k x k ;

[0071] In the ordered Logit model, the last category is used as the benchmark level for comparison. p1, p2, and p j-1 These are the probabilities of the dependent variable taking the first, second, and (j-1)th classes, respectively.

[0072] As can be seen from the model, it divides the dependent variable into two levels sequentially. Except for the constant term (α), which changes continuously, the coefficients (β) of all independent variables remain constant in all Logit models. Therefore, the calculated OddsRatio (OR) represents the probability (ratio of the probability of an event occurring to the probability of it not occurring) of the dependent variable rising one level for every unit change in the independent variable compared to before the change.

[0073] A first ordered Logit model, which has one less number of vegetation cover levels, can be fitted using the first modeling dataset. A second ordered Logit model, which has one less number of soil salinization levels, can be fitted using the second modeling dataset.

[0074] Step S108: In the first ordered Logit model, determine the first burial depth data group that is not significantly correlated with the change in vegetation cover level, and determine the lower limit boundary value of the first burial depth data group as the lower limit of ecological degradation.

[0075] Step S110: In the second ordered Logit model, determine the last data group at the last burial depth that is not significantly correlated with the change in soil salinization level, and determine the upper limit boundary value of the last data group at the last burial depth as the upper limit of salinization.

[0076] Step S112: The lower limit of ecological degradation and the upper limit of salinization are used as the ecological threshold range of groundwater level in the target area.

[0077] In this embodiment of the invention, a method for determining the ecological threshold of groundwater level in arid areas is provided, comprising: determining the vegetation cover level and soil salinization level at various locations within the target area based on remote sensing image data of the target area; grouping the groundwater level depth data of multiple monitoring points within the target area according to a preset interval to obtain depth data groups for each monitoring point; and associating the depth data group of each monitoring point with the vegetation cover level and soil salinization level corresponding to its spatial location based on a geographic information system to obtain a first modeling dataset of depth data group and vegetation cover level for each monitoring point, and a dataset of depth data group and soil salinization level for each monitoring point. The second modeling dataset is used; a first ordered Logit model is constructed based on the first modeling dataset, and a second ordered Logit model is constructed based on the second modeling dataset; in the first ordered Logit model, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower boundary value of the first burial depth data group is determined as the lower limit of ecological degradation; in the second ordered Logit model, the last burial depth data group that is not significantly correlated with the change in soil salinization level is determined, and the upper boundary value of the last burial depth data group is determined as the upper limit of salinization; the lower limit of ecological degradation and the upper limit of salinization are used as the ecological threshold range of groundwater level in the target area. As described above, the method for determining the ecological threshold of groundwater level in arid areas in this invention simultaneously considers a first ordered Logit model related to vegetation cover level and a second ordered Logit model related to soil salinization level. The resulting ecological threshold range of groundwater level depends on both vegetation cover level and soil salinization level, taking into account their synergistic effect. Furthermore, the soil salinization level depends on remote sensing image data, which has high spatial coverage and is more objective. The use of the ordered Logit model to process the ordered nature of the graded data makes it more scientific. The resulting ecological threshold range of groundwater level has good accuracy and precision, alleviating the technical problems of strong subjectivity and poor accuracy in traditional methods for determining the ecological threshold of groundwater level in arid areas.

[0078] The above provides a brief overview of the method for determining the ecological threshold of groundwater level in arid areas according to the present invention. The specific details involved are described in detail below.

[0079] In an optional embodiment of the present invention, determining the vegetation cover level and soil salinization level at various locations within the target area based on remote sensing image data of the target area specifically includes the following steps:

[0080] (1) Extract the vegetation coverage of each location in the target area using Landsat satellite remote sensing images, and classify the vegetation coverage of each location in the target area to obtain the vegetation coverage level of each location in the target area.

[0081] Specifically, when classifying the vegetation cover at various locations within the target area, if the vegetation cover (NDVI) ≤ 0.1, the vegetation cover is no cover; if 0.1 < vegetation cover (NDVI) ≤ 0.3, the vegetation cover is low cover; if 0.3 < vegetation cover (NDVI) ≤ 0.5, the vegetation cover is medium cover; and if the vegetation cover (NDVI) > 0.5, the vegetation cover is high cover.

[0082] (2) Based on Sentinel-2 satellite remote sensing images, the soil electrical conductivity at each location in the target area is retrieved, and the soil electrical conductivity at each location in the target area is classified to obtain the soil salinization level at each location in the target area.

[0083] Specifically, when classifying the soil electrical conductivity at various locations within the target area, if the soil electrical conductivity (i.e., EC) < 4, the soil salinization level is no salinization, and crops grow normally; if 4 ≤ soil electrical conductivity (i.e., EC) < 8, the soil salinization level is mild salinization, and salt-tolerant crops survive; if 8 ≤ soil electrical conductivity (i.e., EC) < 15, the soil salinization level is moderate salinization, and only halophytes survive; if the soil electrical conductivity (i.e., EC) ≥ 15, the soil salinization level is severe salinization, and vegetation dies.

[0084] In an optional embodiment of the present invention, the burial depth data of each monitoring point is grouped and associated with the vegetation cover level and soil salinization level corresponding to its spatial location based on a geographic information system, specifically including the following steps:

[0085] (1) Generate a circular buffer zone with a preset radius centered on each monitoring point;

[0086] Specifically, when performing a spatial join in ArcGIS, the preset radius can be 500 meters. This embodiment of the invention does not impose a specific limitation on this value.

[0087] (2) Extract the average vegetation cover and dominant salinization level within each circular buffer zone;

[0088] Specifically, the average vegetation cover is the average vegetation cover at all locations within the circular buffer zone. The dominant salinization level can be determined according to preset rules, and will not be specifically limited here.

[0089] (3) Determine the vegetation cover level within the circular buffer zone based on the average vegetation cover;

[0090] Specifically, the determination process is the same as that described above, and will not be repeated here.

[0091] (4) Group and correlate the vegetation cover level and the dominant salinization level in the circular buffer zone with the burial depth data of the corresponding monitoring points.

[0092] In addition, the dominant vegetation cover level in each circular buffer can be extracted (without calculating the average vegetation cover or determining the vegetation cover level in the circular buffer based on the average vegetation cover), and then the vegetation cover level corresponding to the dominant vegetation cover level and the salinization level corresponding to the dominant salinization level in the circular buffer can be directly correlated with the burial depth data of the corresponding monitoring points.

[0093] In an optional embodiment of the present invention, after constructing a first ordered Logit model based on a first modeling dataset and a second ordered Logit model based on a second modeling dataset, the method further includes the following steps:

[0094] (1) Validate the first ordered Logit model and the second ordered Logit model to obtain the first ordered Logit model and the second ordered Logit model that pass the validation;

[0095] Specifically, the prerequisite for applying a logistic regression model is to avoid multicollinearity among the independent variables. Therefore, the first step is to test the parallelism of the independent variables. Secondly, the regression coefficients (i.e., β) need to be tested, and the Wald statistic can be used to test the significance of the regression coefficients. When testing the significance of the constructed model, since the model includes categorical data, its pseudo-R-squared is usually not very high. Therefore, model fitting information and goodness-of-fit tests can be used to determine the goodness of model fit.

[0096] (2) In the first ordered Logit model that has been verified, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower limit boundary value of the first burial depth data group is determined as the lower limit of ecological degradation.

[0097] (3) In the validated second ordered Logit model, the last data group with no significant correlation with the change in soil salinization level was determined, and the upper limit boundary value of the last data group with the last depth was determined as the upper limit of salinization.

[0098] In an optional embodiment of the present invention, no significant correlation is defined as the P-value of the regression coefficient of the corresponding variable of the burial depth data group in the first or second ordered Logit model being greater than 0.1, and the confidence interval of the regression coefficient containing 0.

[0099] Specifically, assuming we divide the groundwater level depth (GWD) into multiple groups at 0.5m intervals and introduce them as classification variables into an ordered Logit model. After fitting, we obtain the model results shown in the table below (only the core part is shown):

[0100]

[0101] P-value interpretation: The smaller the P-value (<0.1), the less likely the association between the group and vegetation cover is caused by random error, that is, the more significant the association.

[0102] Analysis process:

[0103] We examined the significance of each burial depth group one by one, from shallow water to deep water.

[0104] In the 3.0-3.5m range, the P-values ​​were all less than 0.1, indicating that water level changes have a significant impact on vegetation cover within this depth range. The deeper the water level (the larger the GWD group value), the more significantly the probability of vegetation cover changing to a lower level increases (because β is a positive value).

[0105] When the water level reaches a depth of 3.5-4.0m, the P-values ​​all begin to be greater than 0.1 (e.g., 0.12, 0.18, 0.25). This means that once the water level reaches a depth of 3.5 meters or less, further declines in the water level no longer have a statistically significant negative impact on vegetation cover. Vegetation cover has stabilized at a very low level and will not experience statistically significant deterioration due to further water level drops.

[0106] Determine the lower limit of ecological degradation (H_min):

[0107] Find the groundwater group where NDVI and burial depth are not significantly correlated, and determine the burial depth of the groundwater level corresponding to the group as the lower limit of ecological degradation.

[0108] In the above table, the first group that is not significant in all three models is "3.5 - 4.0m".

[0109] Therefore, we determine the lower limit (3.5m) of this group as the lower limit of ecological degradation H_min.

[0110] Ecological explanation: This depth (3.5m) is considered the limit depth that the main vegetation roots in this arid area can reach. When the groundwater level depth exceeds 3.5 meters, the vegetation can no longer effectively absorb groundwater, thus degrading and maintaining at a stable low level state. No matter how much deeper the water level is, the situation will not get worse (in a statistical sense). Therefore, 3.5m is the lower limit of the regional groundwater level depth, and the management goal is not to let the regional groundwater level depth exceed this lower limit.

[0111] In an alternative embodiment of the present invention, the method further includes the following steps:

[0112] (1) Obtain the real-time groundwater level depth data of the target area;

[0113] (2) Compare the real-time groundwater level depth data with the ecological threshold interval of the groundwater level;

[0114] (3) If the real-time groundwater level depth data exceeds the ecological threshold interval of the groundwater level, generate and output the corresponding regulation instruction.

[0115] Specifically, the ecological threshold interval of the groundwater level can be expressed as: [H_min, H_max], the real-time groundwater level depth data is expressed as H. If H < H_min → trigger an alarm to reduce exploitation; if H > H_max → start artificial recharge. The above regulation instructions are directly connected to the regional groundwater automatic regulation facilities to achieve closed-loop automatic control.

[0116] In an alternative embodiment of the present invention, the target area is an arid area.

[0117] Comparative advantages with traditional technologies:

[0118]

[0119] The solution of the present invention has the following innovation points:

[0120] Two-factor coupling analysis: Synchronously correlate NDVI (lower limit of ecological degradation) and salinization (upper limit of salinization);

[0121] Ordered Logit model: Solve the statistical bias of traditional linear models in modeling hierarchical dependent variables;

[0122] GIS Spatial Automation: Achieve spatial matching of data through ArcGIS to eliminate errors from manual sampling.

[0123] The solution of the present invention has the following effects:

[0124] 1. Address the statistical bias of traditional models for hierarchical data and improve threshold accuracy;

[0125] Existing technical problem: Traditional linear regression models force ordinal discrete variables such as vegetation cover (NDVI) and salinity level to be modeled as continuous variables, violating the underlying statistical assumptions. This leads to:

[0126] Incorrect estimation of the relationship between variables (e.g., equating the difference between "moderate salinization" and "severe salinization" with the numerical difference); threshold determination is significantly affected by outliers (e.g., errors in individual sampling points cause threshold shifts ≥0.5m);

[0127] The improved mechanism of this invention is to strictly follow the probability distribution characteristics of hierarchical data through an ordered Logit model: NDVI / saltification level is regarded as an ordered categorical variable, and the parameters are solved using a logistic model.

[0128] 2. Break through spatial scale limitations to achieve continuous evaluation across the entire region;

[0129] Existing technical problems: Manual sampling methods are limited by cost and accessibility: per 100km 2 Setting up only 1-2 sampling points is insufficient to capture the spatial heterogeneity of salinization. Point data and area NDVI are difficult to spatially match, introducing boundary errors >30%.

[0130] The improved mechanism of this invention is as follows: a multi-source data coupling channel is constructed through the ArcGIS spatial matching engine: a 500m radius buffer is generated with the monitoring well as the center, and the mean NDVI value and salinization inversion value within the buffer are extracted; continuous classification of salinization across the entire area is achieved using Sentinel-2 imagery with a resolution of 10m.

[0131] 3. Two-factor synergistic constraints mitigate the risks of single-indicator decision-making;

[0132] Existing technical problems: The single-indicator method leads to the failure of ecological management: When only the burial depth > H_min is controlled: shallow water level causes salinization to spread; when only the burial depth < H_max is controlled: deep water level causes vegetation degradation.

[0133] The improved mechanism of this invention is to innovatively introduce a dual threshold interval constraint: the lower limit of ecological degradation H_min is determined by the NDVI-depth model to determine the critical point of vegetation survival; the upper limit of salinization H_max is determined by the salinization-depth model to determine the inflection point of soil salt accumulation; and the final ecological water level threshold is defined as a closed interval [H_min, H_max].

[0134] 4. Drive real-time management and control, and reduce the delay in response to ecological disasters;

[0135] Existing technical problems: The long cycle of manual analysis leads to: when the monthly water level fluctuation in arid areas reaches 1.2m, control instructions are delayed by ≥2 months; the rate of missed disaster warnings is >40%.

[0136] The improved mechanism of this invention is as follows: a groundwater intelligent management and control system is constructed: real-time monitoring well data is transmitted to the cloud via the Internet of Things; the threshold comparison module performs an interval verification of burial depth and [H_min, H_max] every 10 minutes; and an adjustment command is automatically triggered when the threshold is exceeded.

[0137] Summary of technical effects:

[0138] Ordered Logit models address statistical model biases and improve threshold accuracy.

[0139] GIS spatial matching engine replaces manual sampling → breaking through spatial limitations;

[0140] Dual threshold interval generation module → Avoids the risk of single-indicator decision-making;

[0141] Intelligent management and control system verifies in real time → reduces disaster response time.

[0142] Example 2:

[0143] This invention also provides a device for determining the ecological threshold of groundwater level in arid areas. This device is mainly used to execute the method for determining the ecological threshold of groundwater level in arid areas provided in Embodiment 1 of this invention. The following is a detailed description of the device for determining the ecological threshold of groundwater level in arid areas provided in this invention.

[0144] Figure 2 This is a schematic diagram of a device for determining the ecological threshold of groundwater level in arid areas according to an embodiment of the present invention, as shown below. Figure 2 As shown, the device mainly includes: a determination and grouping unit 10, an association unit 20, a construction unit 30, a first determination unit 40, a second determination unit 50, and a setting unit 60, wherein:

[0145] The determination and grouping unit is used to determine the vegetation cover level and soil salinization level of each location in the target area based on the remote sensing image data of the target area, and to group the groundwater level depth data of multiple monitoring points in the target area according to a preset interval to obtain the depth data group of each monitoring point.

[0146] The association unit is used to associate the burial depth data group of each monitoring point with the vegetation cover level and soil salinization level corresponding to its spatial location based on the geographic information system, so as to obtain the first modeling dataset of the burial depth data group and the vegetation cover level of each monitoring point and the second modeling dataset of the burial depth data group and the soil salinization level of each monitoring point.

[0147] A building unit is used to build a first ordered Logit model based on a first modeling dataset and a second ordered Logit model based on a second modeling dataset.

[0148] The first determining unit is used to determine the first burial depth data group that is not significantly correlated with the change in vegetation cover level in the first ordered Logit model, and to determine the lower limit boundary value of the first burial depth data group as the lower limit of ecological degradation.

[0149] The second determining unit is used to determine the last burial depth data group that is not significantly correlated with the change in soil salinization level in the second ordered Logit model, and to determine the upper limit boundary value of the last burial depth data group as the salinization upper limit.

[0150] The setting unit is used to set the lower limit of ecological degradation and the upper limit of salinization as the ecological threshold range of groundwater level in the target area.

[0151] In this embodiment of the invention, a device for determining the ecological threshold of groundwater level in arid areas is provided, comprising: determining the vegetation cover level and soil salinization level at various locations within the target area based on remote sensing image data of the target area; grouping the groundwater level depth data of multiple monitoring points within the target area according to a preset interval to obtain depth data groups for each monitoring point; and associating the depth data group of each monitoring point with the vegetation cover level and soil salinization level corresponding to its spatial location based on a geographic information system to obtain a first modeling dataset of depth data group and vegetation cover level for each monitoring point, and a dataset of depth data group and soil salinization level for each monitoring point. The second modeling dataset is used; a first ordered Logit model is constructed based on the first modeling dataset, and a second ordered Logit model is constructed based on the second modeling dataset; in the first ordered Logit model, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower boundary value of the first burial depth data group is determined as the lower limit of ecological degradation; in the second ordered Logit model, the last burial depth data group that is not significantly correlated with the change in soil salinization level is determined, and the upper boundary value of the last burial depth data group is determined as the upper limit of salinization; the lower limit of ecological degradation and the upper limit of salinization are used as the ecological threshold range of groundwater level in the target area. As described above, the groundwater level ecological threshold determination device for arid areas of the present invention simultaneously considers a first ordered Logit model related to vegetation cover level and a second ordered Logit model related to soil salinization level. The resulting groundwater level ecological threshold interval depends on both vegetation cover level and soil salinization level, taking into account their synergistic effect. Furthermore, the soil salinization level depends on remote sensing image data, which has high spatial coverage and is more objective. The use of ordered Logit model to process the ordered nature of graded data makes it more scientific. The resulting groundwater level ecological threshold interval has good accuracy and high precision, alleviating the technical problems of strong subjectivity and poor accuracy in traditional groundwater level ecological threshold determination methods for arid areas.

[0152] Optionally, the determination and grouping unit is also used to: extract vegetation cover at each location within the target area using Landsat satellite remote sensing images, and classify the vegetation cover at each location within the target area to obtain the vegetation cover level at each location within the target area; and retrieve soil electrical conductivity at each location within the target area based on Sentinel-2 satellite remote sensing images, and classify the soil electrical conductivity at each location within the target area to obtain the soil salinization level at each location within the target area.

[0153] Optionally, the association unit is also used to: generate a circular buffer zone with a preset radius centered on each monitoring point; extract the average vegetation cover and dominant salinization level within each circular buffer zone; determine the vegetation cover level within the circular buffer zone based on the average vegetation cover; and associate the vegetation cover level and the salinization level corresponding to the dominant salinization level within the circular buffer zone with the burial depth data of the corresponding monitoring points.

[0154] Optionally, the device is also used to: validate the first ordered Logit model and the second ordered Logit model to obtain a validated first ordered Logit model and a validated second ordered Logit model; determine the first burial depth data group that is not significantly correlated with the change in vegetation cover level in the validated first ordered Logit model, and determine the lower boundary value of the first burial depth data group as the lower limit of ecological degradation; determine the last burial depth data group that is not significantly correlated with the change in soil salinization level in the validated second ordered Logit model, and determine the upper boundary value of the last burial depth data group as the upper limit of salinization.

[0155] Optionally, no significant correlation is defined as the p-value of the regression coefficient of the corresponding variable in the first or second ordered Logit model for the burial depth data group being greater than 0.1, and the confidence interval of the regression coefficient including 0.

[0156] Optionally, the device is also used to: acquire real-time groundwater level depth data of the target area; compare the real-time groundwater level depth data with the groundwater level ecological threshold range; and if the real-time groundwater level depth data exceeds the groundwater level ecological threshold range, generate and output the corresponding control command.

[0157] Optionally, the target area can be an arid region.

[0158] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0159] like Figure 3 As shown in the embodiment of this application, an electronic device 600 includes a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions that can be executed by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 via the bus. The processor 601 executes the machine-readable instructions to perform the steps of the above-described method for determining the ecological threshold of groundwater level in arid areas.

[0160] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned method for determining the ecological threshold of groundwater level in arid areas.

[0161] The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 601 or by instructions in software form. The processor 601 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 602, and processor 601 reads the information from memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0162] Corresponding to the above-mentioned method for determining the ecological threshold of groundwater level in arid areas, this application embodiment also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and run by a processor, the machine-executable instructions cause the processor to perform the steps of the above-mentioned method for determining the ecological threshold of groundwater level in arid areas.

[0163] The device for determining the ecological threshold of groundwater level in arid areas provided in this application embodiment can be specific hardware on a device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0164] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0165] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0168] If the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method for determining the ecological threshold of groundwater level in arid areas described in various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0169] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0170] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for determining the ecological threshold of groundwater level in arid areas, characterized in that, include: Based on the remote sensing image data of the target area, determine the vegetation cover level and soil salinization level of each location in the target area, and group the groundwater level depth data of multiple monitoring points in the target area according to a preset interval to obtain the depth data group of each monitoring point. Based on the geographic information system, the burial depth data of each monitoring point is grouped and associated with the vegetation cover level and soil salinization level corresponding to its spatial location to obtain the first modeling dataset of burial depth data group and vegetation cover level of each monitoring point and the second modeling dataset of burial depth data group and soil salinization level of each monitoring point. A first ordered Logit model is constructed based on the first modeling dataset, and a second ordered Logit model is constructed based on the second modeling dataset; In the first ordered Logit model, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower limit boundary value of the first burial depth data group is determined as the lower limit of ecological degradation. In the second ordered Logit model, the last data group at the burial depth that is not significantly correlated with the change in the soil salinization level is determined, and the upper limit boundary value of the last data group at the burial depth is determined as the upper limit of salinization. The lower limit of ecological degradation and the upper limit of salinization are used as the ecological threshold range of groundwater level in the target area; Wherein, the lack of significant correlation means that the p-value of the regression coefficient of the corresponding variable of the burial depth data group in the first or second ordered Logit model is greater than 0.1, and the confidence interval of the regression coefficient includes 0.

2. The method according to claim 1, characterized in that, Based on remote sensing image data of the target area, determine the vegetation cover level and soil salinization level at various locations within the target area, including: The vegetation cover at each location within the target area is extracted using Landsat satellite remote sensing imagery, and the vegetation cover at each location within the target area is classified to obtain the vegetation cover level at each location within the target area. Soil electrical conductivity at each location within the target area is retrieved based on Sentinel-2 satellite remote sensing imagery, and the soil electrical conductivity at each location within the target area is classified to obtain the soil salinization level at each location within the target area.

3. The method according to claim 1, characterized in that, Based on a geographic information system, the burial depth data of each monitoring point is grouped and associated with the vegetation cover level and soil salinization level corresponding to its spatial location, including: A circular buffer zone with a preset radius is generated, centered on each of the aforementioned monitoring points; Extract the average vegetation cover and dominant salinity level within each of the circular buffer zones; The vegetation coverage level within the circular buffer zone is determined based on the average vegetation coverage. The vegetation cover level within the circular buffer zone and the salinization level corresponding to the dominant salinization level are grouped and correlated with the burial depth data of the corresponding monitoring points.

4. The method according to claim 1, characterized in that, After constructing a first ordered Logit model based on the first modeling dataset and a second ordered Logit model based on the second modeling dataset, the method further includes: The first ordered Logit model and the second ordered Logit model are verified to obtain the first ordered Logit model and the second ordered Logit model that pass the verification. In the first ordered Logit model that has passed the verification, the first burial depth data group that is not significantly correlated with the change in vegetation cover level is determined, and the lower limit boundary value of the first burial depth data group is determined as the lower limit of ecological degradation. In the validated second ordered Logit model, the last data group at the burial depth that is not significantly correlated with the change in soil salinization level is determined, and the upper limit boundary value of the last data group at the burial depth is determined as the upper limit of salinization.

5. The method according to claim 1, characterized in that, The method further includes: Obtain real-time groundwater level depth data for the target area; Compare the real-time groundwater level depth data with the groundwater level ecological threshold range; If the real-time groundwater level depth data exceeds the groundwater level ecological threshold range, a corresponding control command will be generated and output.

6. The method according to claim 1, characterized in that, The target area is an arid region.

7. A device for determining the ecological threshold of groundwater level in arid areas, characterized in that, include: The determination and grouping unit is used to determine the vegetation cover level and soil salinization level of each location in the target area based on the remote sensing image data of the target area, and to group the groundwater level depth data of multiple monitoring points in the target area according to a preset interval to obtain the depth data group of each monitoring point. The association unit is used to associate the burial depth data group of each monitoring point with the vegetation cover level and the soil salinization level corresponding to its spatial location based on the geographic information system, so as to obtain the first modeling dataset of the burial depth data group of each monitoring point and the vegetation cover level and the second modeling dataset of the burial depth data group of each monitoring point and the soil salinization level. A building unit is configured to build a first ordered Logit model based on the first modeling dataset and a second ordered Logit model based on the second modeling dataset. The first determining unit is used to determine the first burial depth data group that is not significantly correlated with the change in vegetation cover level in the first ordered Logit model, and to determine the lower limit boundary value of the first burial depth data group as the lower limit of ecological degradation. The second determining unit is used to determine the last burial depth data group that is not significantly correlated with the change in the soil salinization level in the second ordered Logit model, and to determine the upper limit boundary value of the last burial depth data group as the salinization upper limit. A setting unit is used to set the lower limit of ecological degradation and the upper limit of salinization as the ecological threshold range of groundwater level in the target area; Wherein, the lack of significant correlation means that the p-value of the regression coefficient of the corresponding variable of the burial depth data group in the first or second ordered Logit model is greater than 0.1, and the confidence interval of the regression coefficient includes 0.

8. An electronic 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, it implements the steps of the method according to any one of claims 1 to 6.

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