A slope stability assessment method and device considering positive and negative effects of vegetation

Abstract

The present invention belongs to the technical field of slope stability assessment, and specifically discloses a slope stability assessment method and device considering the positive and negative effects of vegetation, the method comprising: constructing a slope stability model based on the positive and negative effects of vegetation; quantifying key parameters of the slope: determining a temporal rainfall matrix sequence of different rainfall durations under different regression periods; quantifying the effective rainfall actually reaching the ground according to the interception capacity and evapotranspiration of different vegetation types; determining a terrain humidity index; quantifying the additional root-soil cohesion provided by the action of plant roots; and determining the self-gravity load of soil and vegetation. The present invention can effectively guide disaster prevention and mitigation work related to earthquake-induced landslides in steep terrain areas with complex geographical conditions and significant vegetation diversity.

Description

Technical Field

[0001] The present invention belongs to the technical field of slope stability assessment, and in particular, relates to a slope stability assessment method and device taking into account the positive and negative effects of vegetation. Background Art

[0002] Currently, scholars focus on studying how vegetation affects shallow landslides (Liu et al., 2022b; Murgia et al., 2022). The impact of vegetation on slope stability has become a topic of many studies. These impacts can be broadly divided into two categories: positive impacts provided by vegetation, such as root reinforcement (Arnone et al., 2016; Bischetti et al., 2005), root water uptake (Zhu and Zhang, 2019; Zhu and Zhang, 2015), and vegetation interception (Bordoloi and Ng, 2020; Kozak et al., 2007), and negative impacts provided by vegetation, such as root preferential flow (Shao et al., 2015; Wang et al., 2022), vegetation heavy load (Reichenbach et al., 2018; Zou et al., 2021), and wind load (Wang et al., 2019; Zhuang et al., 2022). The main source of infiltration effect of plant roots is the plant roots below the ground, which act as conduits for precipitation infiltration and soil evaporation, promoting soil infiltration (Liu et al., 2022a; Wang et al., 2022a). Qin et al. (2022) found that when landslides are triggered by rainfall, the hydraulic permeability of the root soil layer above the sliding surface is 14-17 times that of the soil below, resulting in the acceleration of rainfall infiltration and the formation of high pore water pressure zones at the end of the root channels. In addition, root water absorption during plant transpiration can induce capillary negative pressure, which can help improve slope stability and be maintained after a certain rainfall period, as studied by Ng et al. (2014) and Zhu and Zhang (2019; 2015). On the other hand, Kim et al. (2013) found that rainfall interception has little effect on pore water pressure infiltration based on simulation results. In addition, wind loads applied to the trunks of vegetation and indirectly acting on the soil can also affect slope stability (Wang et al., 2013; Zhuang et al., 2022). Therefore, the impact of vegetation on slope stability can be both positive and negative, which makes it difficult to model the stability of shallow slopes when considering these combined factors (Bordoloiand Ng, 2020; Liu et al., 2022b; Murgia et al., 2022).

[0003] The effect of vegetation on slope stability has been a hot topic of research and can be quantified using numerical models (Liu et al., 2016; Murgia et al., 2022; Schwarz et al., 2010; Wu and Sidle, 1995). The reinforcing effect of roots can be quantified by the additional soil cohesion, which increases the shear strength of the soil (Wu and Sidle, 1995). Chirico et al. (2013) and Schwarz et al. (2010) used the Wu model and the fiber bundle model (WM and FBM, respectively) to quantify the reinforcing effect of roots. The heavy load of vegetation is usually quantified using the volume of trees, such as in the study by Zou et al. (2021), who proposed a slope stability model that takes into account the reinforcing effect of roots and the influence of heavy load of vegetation on slope stability through the finite element strength reduction method. Modeling methods mainly include finite element method (Qin et al., 2021; Vandromme et al., 2015; Zhu et al., 2022) and quasi-static analysis method based on infinite slope theory (Keles and Nefeslioglu, 2021; Pack et al., 2005). The former is a more sophisticated method that can better simulate the impact of vegetation on slope stability, but it is limited by the need for high parameter requirements and calibration. The latter is easy to apply and has stronger universality and can be extended to larger regional scales. Although scholars have widely discussed the impact of vegetation on slope stability, due to the complexity of the failure mechanism of covered slopes, modeling slope stability by comprehensively considering the positive and negative effects of vegetation at a regional scale still needs further research.

[0004] Landslides are one of the most dangerous natural disasters, causing a large number of casualties and property losses (Garcia-Delgado et al., 2022). According to incomplete statistics, landslides have caused more than 50,000 deaths in the 21st century (Ha et al., 2020; Mizutori, 2018), and the economic losses caused by landslides worldwide amount to billions of dollars each year. Shallow landslides triggered by rainfall are unstable slopes with small volumes of soil (volumes between 101 and 105 cubic meters) (Liu et al., 2022b). They are densely distributed in the upstream catchment area, contributing a large amount of loose material to the river channel (Bordoni et al., 2021), and then evolve into catastrophic debris flows, causing huge damage to farmland and civil engineering infrastructure, and sometimes causing a large number of casualties (Grima et al., 2020; Pradhan et al., 2019). Therefore, using susceptibility assessment methods to predict or identify areas prone to shallow landslides is very beneficial for land use planning, infrastructure development, community resilience, and disaster mitigation (Camera et al., 2021; Dou et al., 2019).

[0005] For decades, scholars have conducted extensive research to predict the susceptibility of shallow landslides in mountainous areas (Bordoni et al., 2021; Daneshvar, 2014; Feng et al., 2020; Ghosh et al., 2011). The main methods include: expert experience method, data-driven method and deterministic method. Expert experience method allows experience and judgment to determine the spatial extent of the hazard (Hess et al., 2017). Due to the high reliance on expert experience and subjective judgment, the results obtained by these methods may be controversial (Nguyen et al., 2020a). Due to the development of data mining algorithms and GIS tools, data-driven methods are increasingly being used in landslide susceptibility research, predicting landslide susceptibility by fitting the relationship between historical landslide events and the conditions that control landslide occurrence (Line et al., 2021; Zêzere et al., 2017). Unlike expert experience methods, data-driven methods are more objective and quantitative (Yong et al., 2022). In addition, deterministic methods are often used to study landslide formation mechanisms and estimate landslide susceptibility (Liu et al., 2016; Murgia et al., 2022). These physical models are proposed to obtain slope stability, that is, to calculate the ratio of the force that destabilizes the slope to the force that promotes landslide formation (Arnone et al., 2016). Due to the need for detailed input data and parameter calibration, these physical methods are usually only applicable to local scales and are difficult to use for regional scale studies (Baum et al., 2008; Keles and Nefeslioglu, 2021; Murgia et al., 2022; Yong et al., 2022). Summary of the invention

[0006] The purpose of the present invention is to overcome the technical problems raised by the background technology and propose a slope stability assessment method that takes into account the positive and negative effects of vegetation to effectively assess the stability of covered slopes under extreme rainfall scenarios.

[0007] The specific technical solutions of the present invention are as follows:

[0008] According to a first aspect of the present invention, a slope stability assessment method considering positive and negative effects of vegetation is provided, the method comprising:

[0009] Based on the edgeless slope model, the slope stability model is constructed by comprehensively considering the positive and negative effects of vegetation;

[0010] Quantification of key parameters of slope cover:

[0011] Determine the temporal rainfall matrix sequence with different rainfall durations under different regression periods;

[0012] Quantify the effective rainfall that actually reaches the ground based on the interception capacity of different vegetation types and evapotranspiration;

[0013] Determine the terrain wetness index;

[0014] Quantify the additional root-soil cohesion provided by plant roots;

[0015] Determine the self-gravity loads of soil and vegetation.

[0016] Furthermore, the slope stability model constructed based on the positive and negative effects of vegetation is shown in formula (1):

[0017] (1)

[0018] in, F S is the slope safety factor, C soil +C rt It represents the root-soil cohesion. b represents the side width of the evaluation unit, W soil Represents the external load weight of vegetation, W soil represents the weight of soil, m represents the topographic wetness index ( TWI ), h represents the depth of the sliding surface, γ w represents the water density, β represents the slope angle, φ represents the internal friction angle of soil, T E Represents earthquake force.

[0019] Furthermore, the determining of the time series rainfall matrix sequence of different rainfall durations under different regression periods specifically includes:

[0020] The Gouber model is constructed to obtain the cumulative intensity-frequency-duration curve of rainfall events. The Gouber model cumulative distribution is determined based on formula (2):

[0021] (2)

[0022] in, η and λ The scale and location parameters representing the form of the Guber model, z Represents the cumulative rainfall within a certain rainfall period;

[0023] The return period of extreme rainfall events is expressed as:

[0024] (3a)

[0025] The probability weighted moment method is used to determine η and λ :

[0026] (3b)

[0027] (4)

[0028] in, b 0 and b 1 refers to the first and second overall PWM of the Guber model, respectively;

[0029] for r >0th r samples, and the probability weighted moment is obtained by the following formula:

[0030] (5)

[0031] in, n represents the sample size, including the annual maximum value of a specific rainfall duration, variable j represents the ascending order of samples, ≤ ≤…≤… represents the sequence of annual maximum values ​​in ascending order;

[0032] Integrating equations (3b)-(5), we use equations (6)-(7) to determine the scale and location parameters of the estimated Guber model in the form:

[0033] (6)

[0034] (7)

[0035] Reducing the time scale of rainfall events using a bounded stochastic cascade model.

[0036] Furthermore, the method of reducing the time scale of rainfall events by using a bounded random cascade model specifically includes:

[0037] When the accumulated rainfall is determined, the rainfall intensity of each subunit during the decomposition period is calculated by the following formula:

[0038] (8)

[0039] in, k represents the number of descending steps, i Indicated in k Subinterval rainfall intensity in step-down decomposition,Q 0 is the rainfall depth, which means the cumulative rainfall depth in a specific regression period. W is the sequence of the decline coefficients, i.e., the ratio of the rainfall intensity of the parent interval transferred to the child interval during the decline period, p yes W Function to follow;

[0040] It is characterized by the following formula W :

[0041] (9)

[0042] in, B ( a ) represents the Beta function, including a parameter that varies with time scale a , a greater than zero, a Depending on the size of the time scale considered, it is expressed as:

[0043] (10)

[0044] Among them, k The time scale of the basic unit after subdivision is t = D r / 2 k express, k The value is 1, 2, 3, ..., n , a 0 and H represents a constant parameter;

[0045] The parameters are estimated by the following formula a :

[0046] (11)

[0047] Calculated by the following formula W :

[0048] (12)

[0049] in, Indicates that the time series is recorded at position j The rainfall depth at the time interval is Dr , Nr Indicates the time scale under consideration Dr The total number of records on When equal to zero or undefined, W Not included in the analysis.

[0050] Furthermore, the effective rainfall actually reaching the ground is quantified according to the interception capacity and evapotranspiration of different vegetation types, specifically including:

[0051] Potential evapotranspiration was calculated using the following formula:

[0052] (13)

[0053] in, ET p,t For the t Daily potential evapotranspiration, ET r,t For the t The reference evapotranspiration for the day, K is the crop coefficient, the effects of crop transpiration and soil evaporation are integrated into K middle;

[0054] The reference evapotranspiration is calculated by the following formula:

[0055] (14)

[0056] in, Ra represents extraterrestrial radiation, T avg represents the average daily temperature, and TD represents the diurnal temperature range, defined as the difference between the daily maximum and minimum air temperatures;

[0057] The maximum canopy storage volume is calculated using the following formula:

[0058] (15)

[0059] in, LAI is the leaf area index;

[0060] The leaf area index was calculated using the following formula:

[0061] (16)

[0062] in, LAI max is the maximum leaf area index, FPAR is the photosynthetic radiation fraction, FPAR max is the maximum photosynthetic radiation fraction;

[0063] The photosynthetic radiation fraction is calculated by the following formula:

[0064] (17)

[0065] in, SR is the photosynthetic radiation term, which is equal to , SR max for Maximum SR value , SR min for Minimum SR value , NDVI is the normalized difference vegetation index, FPAR max and FPAR min The values ​​of are 0.95 and 0.001 respectively;

[0066] Update canopy water storage based on the day's precipitation:

[0067] (18)

[0068] Among them, Scan t is the crown storage volume on day t, Scan t−1 is the crown storage on day t−1, P t is the precipitation on day t

[0069] Effective precipitation is the water that cannot be stored in the tree canopy and is calculated using the following formula:

[0070] (19)

[0071] in, P t represents the effective precipitation on day t, Scant represents the canopy water storage on day t;

[0072] After calculating the effective precipitation, the canopy water storage is updated as:

[0073] (20)

[0074] The intercept is calculated by the following formula:

[0075] (twenty one)

[0076] in, Int t (mm) indicates the t The water intercepted by the sky, ET r,t (mm) indicates the t Reference evapotranspiration for the day;

[0077] Update canopy water storage by subtracting the interception:

[0078] (twenty two)

[0079] Furthermore, the terrain humidity index is determined by formulas (23)-(24):

[0080] (twenty three)

[0081] (twenty four)

[0082] in, TWI is the ratio of the water level height above the soil sliding surface, k s-rs represents the saturated hydraulic conductivity of the root-soil complex, which was quantified based on mechanical tests and survey results of different vegetation types in the study area; P e is the effective rainfall, which is the amount of rainfall reaching the surface after vegetation interception and evaporation are subtracted from the total rainfall. T Represents soil permeability, calculated by multiplying the saturated hydraulic conductivity by the soil depth. A up represents the upslope drainage area, B Indicates the width of the flow boundary, sin Represents the head gradient.

[0083] Furthermore, the quantification of the additional root-soil cohesion provided by the plant root system specifically includes:

[0084] The additional cohesion provided by plant roots is calculated by the following formula:

[0085] (25)

[0086] (26)

[0087] (27)

[0088] in, C root It is the additional cohesion provided by the reinforcement effect of plant roots; is the correction factor; It represents the average tensile strength per unit root cross-sectional area; and are shear distortion and internal friction angle, respectively; RAR represents the root area ratio; A r represents the root cross-sectional area; A rs Represents the rooted soil area.

[0089] Furthermore, the self-gravity load of soil and vegetation is determined by the following formula:

[0090] (28)

[0091] in, W veg is the gravity load of different vegetation types, W soli The weight of soil.

[0092] According to a second aspect of the present invention, there is provided a slope stability assessment device taking into account the positive and negative effects of vegetation, the device comprising:

[0093] A model building unit is configured to build a slope stability model based on an infinite slope model and taking into account the positive and negative effects of vegetation;

[0094] A parameter quantization unit is configured to quantize key parameters of the coverage slope:

[0095] Determine the temporal rainfall matrix sequence with different rainfall durations under different regression periods;

[0096] Quantify the effective rainfall that actually reaches the ground based on the interception capacity of different vegetation types and evapotranspiration;

[0097] Determine the terrain wetness index;

[0098] Quantify the additional root-soil cohesion provided by the anchoring effect of plant roots;

[0099] Determine the self-gravity loads of soil and vegetation.

[0100] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the slope stability assessment method considering the positive and negative effects of vegetation as described in various embodiments of the present invention.

[0101] The slope stability assessment method, device and medium considering the positive and negative effects of vegetation provided by various embodiments of the present invention have at least the following beneficial effects:

[0102] The present invention can complete the assessment of the stability of covered slopes at a regional scale, and can effectively guide disaster prevention and mitigation work related to earthquake-induced landslides in steep terrain areas with complex geographical conditions and significant vegetation diversity. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0104] Figure 1 A processing flow chart of an unsupervised earthquake brittleness parameter prediction method according to an embodiment of the present invention is shown.

[0105] Figure 2 The spatial density of earthquake intensity zoning and earthquake landslides caused by the Luding earthquake is shown, including (a) point density and (b) surface density.

[0106] Figure 3 The spatial distribution of landslides caused by the “9.5” Luding earthquake is shown, where (a)-(d) and (a')-(d') represent images before and after the earthquake, respectively).

[0107] Figure 4 The spatial distribution of the landslide and the corresponding longitudinal and transverse profiles are shown.

[0108] Figure 5 The ROC curves for different rainfall durations with a 50-year return period are shown.

[0109] Figure 6 The proportions of different stability categories predicted by the ISM-PES-VEG model at different rainfall return periods and rainfall durations are shown.

[0110] Figure 7 Shown are the instability zoning maps for rainfall events with a 50-year return period of (a) 3 h, (b) 6 h, (c) 12 h, and (d) 24 h.

[0111] Figure 8 A structural diagram of an unsupervised earthquake brittleness parameter prediction device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0112] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0113] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the invention.

[0114] The present invention will now be further described with reference to the accompanying drawings.

[0115] The embodiment of the present invention provides a slope stability assessment method taking into account the positive and negative effects of vegetation. Figure 1 As shown, the method includes step S100 and step S200.

[0116] Step S100, based on the infinite slope model, the slope stability model is constructed by comprehensively considering the positive and negative effects of vegetation.

[0117] This embodiment comprehensively considers the positive and negative effects of vegetation and proposes a model based on an infinite slope model to predict the stability of a covered slope. The parameters involved include: seismic force, vegetation load, root reinforcement, pore water pressure ratio, etc. Finally, the calculation formula for slope stability is as follows:

[0118] (1)

[0119] in, F S is the slope safety factor, C soil +C rt It represents the root-soil cohesion. b represents the side width of the evaluation unit, W soil Represents the external load weight of vegetation, W soil represents the weight of soil, m represents the topographic wetness index ( TWI ), h represents the depth of the sliding surface, γ w represents the water density, β represents the slope angle, φ represents the internal friction angle of soil, T E Represents earthquake force.

[0120] Step S200, quantifying key parameters of the coverage slope.

[0121] (1) Determine the random rainfall pattern (determine the time series rainfall matrix sequence with different rainfall durations under different regression periods)

[0122] Based on past observational data, the study used extreme value analysis to predict the possibility of extreme rainfall events. Based on the observed data obtained, the study obtained the rainfall time distribution on a smaller time scale through time downscaling. Then, the bounded random cascade model (BRCM) was used to complete the time downscaling of rainfall data. In other words, the reproduction of rainfall events under the random rainfall pattern was achieved through the following two steps:

[0123] ① The Gumbel model is used to analyze extreme rainfall events.

[0124] The cumulative intensity-frequency-duration (IFD) curves are based on multi-year rainfall data and provide a a , D r )) Given the average rainfall intensity (I a ) and duration (D r ) is a graphical representation of the frequency of occurrence under . Gumbel distribution is a variant of GEV distribution and is usually used to determine the maximum extreme value for predicting extreme events such as rainfall torrential rain and earthquakes (Finlay et al., 1997). Therefore, this paper uses the Gumbel model to obtain the cumulative intensity-frequency-duration of rainfall events. The cumulative distribution of the Gumbel model is determined based on formula (2).

[0125] (2)

[0126] Here, η and λ represent the scale and location parameters of the Gumbel model, while z represents the cumulative rainfall over a certain rainfall duration. The return period of a specific rainfall event, whose rainfall duration and mean rainfall intensity are known, can be expressed as follows:

[0127] (3a)

[0128] Then, the parameters of the Gumbel model η and λ It can be determined using the probability weighted moments (PWMs) method. See Equation (3b)-Equation (4) for details.

[0129] (3b)

[0130] (4)

[0131] Here, b0 and b1 refer to the first and second overall PWM of the Gumbel model, respectively; the scale and location parameters of the Gumbel model are expressed as η and γ ,and γ represents the Euler constant. In addition, for r >0th r The following formula is used to obtain the probability weighted moment.

[0132] (5)

[0133] here, n represents the sample size, which includes the annual maximum value for a specific rainfall duration. j represents the ascending order of samples, while ≤ ≤…≤… represents the sequence of annual maximum values ​​in ascending order.

[0134] By integrating Equations (3b)-(5), the estimated Gumbel model parameters can be determined using Equations (6)-(7) η and γ :

[0135] (6)

[0136] (7)

[0137] ② Use the bounded random cascade model (BRCM) to reduce the time scale of rainfall events.

[0138] However, the previous step only yields the cumulative rainfall depth of a single rainfall event. Therefore, it is necessary to temporally downscale the single rainfall event to obtain the rainfall distribution value on a smaller time scale. This can be achieved by considering the (random) self-affine properties of the rainfall time series to build a bounded random cascade model (BRCM). The temporal scale of the rain events can be downscaled using a splitting coefficient, where each scaling process involves dividing the parent interval into two equal sub-intervals. The rainfall intensity of the sub-interval is then calculated by multiplying the rainfall intensity of the parent interval by the corresponding splitting coefficient. Therefore, once the cumulative rainfall is determined, the splitting coefficient can be used W Calculate the rainfall intensity for the subunits for each decomposition period.

[0139] (8)

[0140] Here, k represents the number of descending steps, i Indicated in k Subinterval rainfall intensity in step-down decomposition, Q 0 is the rainfall depth, which represents the cumulative rainfall depth under the specific regression period in the previous section, W is the sequence of the decline coefficients, i.e., the ratio of the rainfall intensity of the parent interval transferred to the child interval during the decline period, p yes W Follow the function.

[0141] The decomposition coefficient W can be characterized using Beta distribution:

[0142] (9)

[0143] Here, B(a) represents the Beta function, which includes a parameter that varies with time scale a ,in a is greater than zero. In addition, a Depending on the size of the time scale considered, it can be further expressed as:

[0144] (10)

[0145] Here, k The time scale of the basic unit after subdivision is t = D r / 2 k Indicates that k The value is 1, 2, 3,..., n , a 0 and H Represents a constant parameter.

[0146] The parameter a can be estimated using the sample moment method:

[0147] (11)

[0148] in, W Represents the sample splitting factor that can be calculated using non-overlapping adjacent pairs of rainfall measurements.

[0149] (12) Here, Indicates that the time series is recorded at position j The rainfall depth at the time interval is Dr , Nr represents the total number of records on the considered time scale Dr. When zero or undefined, W is not included in the analysis.

[0150] (2) Vegetation interception model

[0151] Due to the interception effect of vegetation on the covered slope on rainfall, quantifying the effective rainfall that actually reaches the ground requires considering the interception capacity of different vegetation types and evapotranspiration. The steps are as follows:

[0152] ① Potential evapotranspiration ET

[0153] Potential evapotranspiration can be determined by the crop coefficient method, which involves two parameters: the surface evapotranspiration rate ( ET r ) and the crop coefficient reflecting crop characteristics ( K ), the calculation formula is as follows:

[0154] (13)

[0155] in, ET p、t (mm) is the potential evapotranspiration on day t, ET r,t(mm) is the reference evapotranspiration on day t, K (-) is the crop coefficient. The effects of crop transpiration and soil evaporation are integrated into Kc, which can be obtained from Allen et al. (1998). ET r The evapotranspiration rate is a reference surface that has enough water to allow evapotranspiration to occur at the potential rate. The reference surface is a hypothetical grass reference crop with specific characteristics. Potential evapotranspiration ET p It is not limited by factors such as soil water and salt stress, crop density, pests and diseases, weed invasion or low fertility.

[0156] Among them, reference evapotranspiration ( ET r ) is calculated using the modified Hargreaves equation that depends only on temperature, and the calculation formula is as follows:

[0157] (14)

[0158] Where, Ra (MJ m −2 day −1 ) represents the extraterrestrial radiation, T avg (°C) represents the mean daily air temperature, and TD (°C) represents the diurnal temperature range, defined as the difference between the daily maximum and minimum air temperatures. The constant 0.408 is used to convert the units to mm, and Ra can be obtained from the table (Allen et al., 1998) or the formula (using the day of the year and the latitude of the study area).

[0159] ② Maximum canopy storage volume (Scanmax (mm))

[0160] Research has shown that there are many different relationships between Scanmax and LAI, and the maximum canopy savings can be calculated using the following formula:

[0161] (15)

[0162] in, LAI is the leaf area index.

[0163] Then, the LAI of a uniformly distributed vegetation on the surface can be calculated using the logarithmic relationship between LAI and the fractional photosynthetic radiation (FPAR) as follows:

[0164] (16)

[0165] Among them, LAI(-) is the leaf area index, LAI max(-) is the maximum leaf area index (related to vegetation type). The maximum and minimum values ​​of LAI are related to the maximum and minimum values ​​of FPAR. Table 1 gives the LAI of a certain number of vegetation types. max value.

[0166]

[0167] The fraction of photosynthetic radiation (FPAR) can be calculated by the relationship between NDVI and FPAR, and the calculation formula is as follows:

[0168] (17)

[0169] in, SR is the photosynthetic radiation term, which is equal to , SR max for Maximum SR value , SR min for Minimum SR value , NDVI is the normalized difference vegetation index, FPAR max and FPAR min The values ​​of are 0.95 and 0.001 respectively;

[0170] ③ Vegetation interception and effective rainfall

[0171] If interception is calculated on a daily basis, it consists of the daily precipitation plus the intercepted water remaining in the canopy storage from the previous day. First, update the canopy storage based on the current day's precipitation:

[0172] (18)

[0173] Among them, Scan t (mm) is the crown storage volume on day t, Scan t−1 (mm) is the crown storage volume on day t−1, P t (mm) is the precipitation on day t. The water that cannot be stored in the tree canopy is called net precipitation, or effective precipitation.

[0174] (19)

[0175] in, P t (mm) represents the effective precipitation on day t, Scant(mm) represents the canopy water storage on day t. This equation shows that net precipitation will only occur when the canopy water storage exceeds the maximum canopy water storage. After calculating the effective precipitation, the canopy water storage is updated to

[0176] (20)

[0177] The remaining water stored in the canopy can be intercepted by the atmosphere, and the amount of water intercepted depends on the atmospheric demand for open water evaporation. A commonly used value for the atmospheric water demand for open water evaporation is 1.5, which is derived from the ratio between 1 and the average planar evaporation coefficient Kp (~ 0.65). Therefore, the interception amount can be calculated by the following formula:

[0178] (twenty one)

[0179] in, Int t (mm) indicates the t The water intercepted by the sky, ET r,t (mm) indicates the t Finally, the canopy water storage is updated by subtracting the interception:

[0180] (twenty two)

[0181] (3) Underground flow model

[0182] Pore ​​water pressure is the water pressure in the soil below the groundwater table. Similar to common infinite slope models (such as SHALSTAB and SINMAP models), the pore water pressure in this paper acts on the soil in a direction perpendicular to the slope surface. The topographic moisture index is a key parameter for quantifying pore water pressure. The topographic moisture index uses Darcy's law and the phenomenon found in field experiments to quantify the proportion of saturated soil layers, that is, the trench catchment area tends to have higher soil moisture. Finally, the topographic moisture index is quantified based on formula (23)-formula (24),

[0183] (twenty three)

[0184] (twenty four)

[0185] Where TWI is the ratio of the water level height above the soil sliding surface, k s-rs represents the saturated hydraulic conductivity of the root-soil complex, which was quantified based on mechanical tests and survey results for different vegetation types in the study area. ef is the effective rainfall, which is the amount of rainfall reaching the surface after vegetation interception and evaporation are subtracted from the total rainfall. T is the soil permeability, which can be calculated by multiplying the saturated hydraulic conductivity by the soil depth. up represents the upslope drainage area, B represents the width of the outflow boundary, sin Represents the head gradient.

[0186] (4) Root reinforcement model (quantifying the additional root-soil cohesion (C root )

[0187] The reinforcement effect provided by plant roots is an important source of strength for vegetated slopes to resist slope failure. The additional cohesion (C root ) can be calculated using Formula (25)-Formula (27).

[0188] (25)

[0189] (26)

[0190] (27)

[0191] in, C root is the additional cohesion provided by the reinforcement effect of plant roots, in [kPa]; It can be considered as a correction factor and is usually specified as 1.2; It represents the average tensile strength per unit root cross-sectional area; and are shear distortion and internal friction angle, respectively; RAR represents root area ratio; A r represents the root cross-sectional area; A rs represents the rooted soil area. These parameters are given in Table 2.

[0192]

[0193] (5) Vegetation load model

[0194] Self-gravity load of soil and vegetation W:

[0195] (28)

[0196] Self-gravity load of different vegetation types W veg

[0197] The quantitative formula for the uniformly distributed load of vegetation deadweight is as follows:

[0198] First, the volume of trees is calculated based on the diameter at breast height and tree height of vegetation. The calculation formula is:

[0199] Diameter at breast height 10cm or more:

[0200] V=0.39(D+3.5)²(0.48+L)÷10000 (29)

[0201] Diameter at breast height less than 10 cm:

[0202] V=0.7854L((D+0.45L)0.2)²÷10000 (30)

[0203] In the formula, V is the forest timber volume (m 3 ), D is the breast diameter (cm), L is the tree height (m). The self-weight of the meadow has almost no effect on the slope stability, so the self-weight of the meadow is not calculated; during the field investigation, it was found that the self-weight of shrubs with a breast diameter of less than 10 cm has no significant effect on the slope stability, so it is not considered in this calculation.

[0204] The vegetation self-weight acts as a uniformly distributed load on the interface of the rock bed. The vegetation load per unit width is calculated as follows:

[0205] (31)

[0206] in, F is a uniformly distributed load, that is W veg , V is the forest timber volume, ρ m is the forest density, g is the acceleration, ρ l is the forest density, S is the slope area, w cell is the width of the soil unit (m) (i.e. s).

[0207] The following embodiments of the present invention will provide specific application cases of the method described above.

[0208] Case study on slope stability in the Luding earthquake affected area:

[0209] (1) Overview of the case study area

[0210] ① Overview of the 9.5 Luding Earthquake

[0211] On September 5, 2022, a shallow earthquake with a magnitude of 6.8 (Ms) occurred in Luding County, Ganzi Prefecture, Sichuan Province (Dengjie et al., 2023). The epicenter of the earthquake was located in Moxi Town, which is located at the southeast end of the Xianshuihe Fault Zone and has an underground depth of 16 kilometers (29.59°N, 102.08°E) (An et al., 2023). The length of the S217 Luding-Shimian section is 59 kilometers. Figure 2 As shown, the area is prone to landslides, mud-rock flows and other mountain hazards, which pose a huge challenge to disaster prevention and control due to the steep terrain, fractured strata and abundant loose materials after earthquakes.

[0212] ② Geographical environment overview

[0213] The magnitude of the "9.5" Luding earthquake was IX, and the epicenter was concentrated in densely populated towns and villages, causing great damage to local life and property (An et al., 2023; Dengjie et al., 2023). Geographically, this area is located in the transition zone between the Qinghai-Tibet Plateau and the Sichuan Basin, and is a typical area of ​​rapid topographic changes. Structurally, the earthquake zone is located at the Y-shaped intersection of three fault zones: the Longmenshan Fault Zone, the Xianshuihe Fault Zone, and the Anninghe Fault Zone. These geological structures are active, with rock fragmentation and joint distribution (Jiang et al., 2022). In terms of lithology, the outcropping strata in the area are Quaternary to Sinian, with widespread distribution of Triassic and Paleozoic Triassic strata and Proterozoic metamorphic rock strata (Zou et al., 2021). In terms of topography, this region has significant differences in altitude, with higher altitudes in the west and lower altitudes in the east. The highest peak in the west of the region, Mount Gongga, is 7,556 meters above sea level, while the altitude in the east exceeds the surface of the Dadu River and is nearly 6,500 meters. In terms of hydrology, the region has rich river systems, deep valleys, dense tributaries and fissured rock masses, which produce conditions for the formation of earthquake landslides due to the unloading effect of river cuts in the forward direction (An et al., 2023). In terms of climate, the region is mainly affected by the southeast monsoon and warm and humid air currents from the Sichuan Basin, and has a mainly subtropical monsoon climate. The average annual temperature in the region is 13.6°C, and the annual precipitation is 1,009.4 mm, mainly concentrated in June to August (The Ministry of Civil Affairs of the People's Republic of China, 2016).

[0214] In addition to the geographical background conditions controlling landslide formation described above, the study area also has significant vegetation diversity (Jiang et al., 2022; Sun et al., 2022; Zou et al., 2021). Vegetation types are mainly distributed in broad-leaved forests, mixed coniferous forests, coniferous forests, shrubs and meadows, ranging from 1000 m to 7556 m above sea level. The significant zonation of vegetation in both horizontal and vertical directions poses a great challenge for landslide prediction in the region. Previous studies have shown that vegetation is closely related to the spatial distribution of landslides (Qin et al., 2021), and more work is needed to better understand the impact of vegetation on slope stability. Therefore, this area is an ideal testing ground to advance the understanding of the seismic failure mechanism of vegetated slopes.

[0215] ③ Spatial distribution of landslides caused by the “9.5” Luding earthquake

[0216] The spatial distribution and number of earthquake landslides were determined based on field surveys and remote sensing interpretation of high-resolution images (including aerial images reconstructed by drones and remote sensing images from GF-6, Beijing-3 and Sentinel-1 satellites). This multi-source approach significantly improves the accuracy of earthquake landslide identification and solves the problem of low image data coverage leading to low identification accuracy (Singhroy, 2009). Images were collected as close as possible to the time of the earthquake to capture the differences in characteristics caused by earthquake landslides, such as Figure 3 To ensure data reliability, the interpretation signs of earthquake landslides, such as texture and color differences, changes in vegetation coverage, and extension of landslide boundaries, were fully considered (Xu, 2015). A total of 7,480 earthquake landslides were identified, and their spatial distribution is shown in Figure 4 shown.

[0217] (2) Results of the assessment of the stability of the covered slope

[0218] The study quantified the model performance based on several indicators, including prediction accuracy (ACC), area under the ROC curve (AUC), precision, f1 score, and recall. The results showed that the above model can evaluate slope stability with an AUC range of 0.873 to 0.884. Figure 5 This indicates that the proposed method can effectively guide disaster prevention and mitigation work related to earthquake-induced landslides in steep terrain areas with complex geographical conditions and significant vegetation diversity.

[0219] Based on the above model results, the slope instability levels were reclassified into five categories (very low, low, medium, high and very high) for various rainfall scenarios, using 1.50, 1.25, 1.00 and 0.50 as cut-off points. Figure 6 The relative distribution of different instability levels predicted by the model under different rainfall scenarios is shown. The results show that the proportion of total susceptible areas under various rainfall duration scenarios increases with the increase in the return period of rainfall. In addition, although the area of ​​high susceptible areas fluctuates under different rainfall durations and return periods, it remains roughly around 27.37%. This shows that the spatial distribution of shallow landslide-prone areas is mainly controlled by the disaster environment, while topographic and geological differences determine the basic distribution of shallow landslide-prone areas.

[0220] The slope instability classification map on the GIS platform is drawn by assigning different visual colors to the grid cells, such as Figure 7 The results show that the locations of high instability and very high instability are mainly distributed on both sides of the Dadu River in the southeast of the epicenter. The steep terrain and abundant loose material sources in these areas are conducive to the formation of focal landslides due to long-term river erosion and weathering.

[0221] The embodiment of the present invention also provides a slope stability assessment device taking into account the positive and negative effects of vegetation. Figure 8 As shown, the device 800 includes:

[0222] The model building unit 801 is configured to build a slope stability model based on an infinite slope model and taking into account the positive and negative effects of vegetation;

[0223] The parameter quantization unit 802 is configured to quantize the key parameters of the coverage slope:

[0224] Determine the temporal rainfall matrix sequence with different rainfall durations under different regression periods;

[0225] Quantify the effective rainfall that actually reaches the ground based on the interception capacity of different vegetation types and evapotranspiration;

[0226] Determine the terrain wetness index;

[0227] Quantify the additional root-soil cohesion provided by the anchoring effect of plant roots;

[0228] Determine the self-gravity loads of soil and vegetation.

[0229] In some embodiments, the model building unit 801 is further configured to build a slope stability model based on the positive and negative effects of vegetation as shown in formula (1):

[0230] (1)

[0231] in, F S is the slope safety factor, C soil +C rt It represents the root-soil cohesion. b represents the side width of the evaluation unit, W soil Represents the external load weight of vegetation, W soil represents the weight of soil, m represents the topographic wetness index ( TWI ), h represents the depth of the sliding surface, γ w represents the water density, β represents the slope angle, φ represents the internal friction angle of soil, T E Represents earthquake force.

[0232] In some embodiments, the parameter quantization unit 802 is further configured to:

[0233] The Gouber model is constructed to obtain the cumulative intensity-frequency-duration of rainfall events. The Gouber model cumulative distribution is determined based on formula (2):

[0234] (2)

[0235] Among them, η and λ represent the scale and location parameters of the Guber model, and z represents the cumulative rainfall within a certain rainfall duration;

[0236] The return period of a particular rainfall event is expressed as:

[0237] (3a)

[0238] The probability weighted moment method is used to determine η and λ:

[0239] (3b)

[0240] (4)

[0241] in, b 0 and b 1 refers to the first and second overall PWM of the Guber model, respectively;

[0242] For the rth sample with r>0, the probability weighted moment is obtained by the following formula:

[0243] (5)

[0244] in, n represents the sample size, including the annual maximum value of a specific rainfall duration, variable j represents the ascending order of samples, ≤ ≤…≤… represents the sequence of annual maximum values ​​in ascending order;

[0245] Integrating equations (3b)-(5), we use equations (6)-(7) to determine the scale and location parameters of the estimated Guber model in the form:

[0246] (6)

[0247] (7)

[0248] Reducing the time scale of rainfall events using a bounded stochastic cascade model.

[0249] In some embodiments, the parameter quantization unit 802 is further configured to:

[0250] When the accumulated rainfall is determined, the rainfall intensity of each subunit during the decomposition period is calculated by the following formula:

[0251] (8)

[0252] in, k represents the number of descending steps, i Indicated in k Subinterval rainfall intensity in step-down decomposition, Q 0 is the rainfall depth, which means the cumulative rainfall depth in a specific regression period. W is the sequence of the decline coefficients, i.e., the ratio of the rainfall intensity of the parent interval transferred to the child interval during the decline period, p yes W Function to follow;

[0253] It is characterized by the following formula W :

[0254] (9)

[0255] in, B ( a ) represents the Beta function, including a parameter that varies with time scale a , a greater than zero, a Depending on the size of the time scale considered, it is expressed as:

[0256] (10)

[0257] Among them, k The time scale of the basic unit after subdivision is t = D r / 2 k express, k The value is 1, 2, 3,..., n , a 0 and H represents a constant parameter;

[0258] The parameters are estimated by the following formula a :

[0259] (11)

[0260] Calculated by the following formula W :

[0261] (12)

[0262] in, Indicates that the time series is recorded at position j The rainfall depth at the time interval is Dr , Nr represents the total number of records on the considered time scale Dr, when When zero or undefined, W is not included in the analysis.

[0263] In some embodiments, the parameter quantization unit 802 is further configured to:

[0264] Potential evapotranspiration was calculated using the following formula:

[0265] (13)

[0266] in, ET p,t For the t Daily potential evapotranspiration, ET r,t For the t The reference evapotranspiration for the day, K is the crop coefficient, the effects of crop transpiration and soil evaporation are integrated into K middle;

[0267] The reference evapotranspiration is calculated by the following formula:

[0268] (14) in, Ra represents extraterrestrial radiation, T avg represents the average daily temperature, and TD represents the diurnal temperature range, defined as the difference between the daily maximum and minimum air temperatures;

[0269] The maximum canopy storage capacity is calculated by the following formula:

[0270] (15)

[0271] in, LAI is the leaf area index;

[0272] The leaf area index was calculated using the following formula:

[0273] (16)

[0274] in, LAI max is the maximum leaf area index, FPAR is the photosynthetic radiation fraction, FPAR max is the maximum photosynthetic radiation fraction;

[0275] The photosynthetic radiation fraction is calculated by the following formula:

[0276] (17)

[0277] in, SR is the photosynthetic radiation term, which is equal to , SR max for Maximum SR value , SR min for Minimum SR value , NDVI is the normalized difference vegetation index, FPAR max and FPAR min The values ​​of are 0.95 and 0.001 respectively;

[0278] Update canopy water storage based on the day's precipitation:

[0279] (18)

[0280] Among them, Scan t is the crown storage volume on day t, Scan t−1 is the crown storage on day t−1, P t is the precipitation on day t

[0281] Effective precipitation is the water that cannot be stored in the tree canopy and is calculated using the following formula:

[0282] (19)

[0283] in, P t represents the effective precipitation on day t, Scant represents the canopy water storage on day t;

[0284] After calculating the effective precipitation, the canopy water storage is updated as:

[0285] (20)

[0286] The intercept is calculated by the following formula:

[0287] (twenty one)

[0288] in, Int t (mm) indicates the t The water intercepted by the sky, ET r,t (mm) indicates the t Reference evapotranspiration for the day;

[0289] Update canopy water storage by subtracting the interception:

[0290] (twenty two)

[0291] In some embodiments, the parameter quantization unit 802 is further configured to determine the terrain wetness index by using formulas (23)-(24):

[0292] (twenty three)

[0293] (twenty four)

[0294] in, TWI is the ratio of the water level height above the soil sliding surface, k s-rs represents the saturated hydraulic conductivity of the root-soil complex, which was quantified based on mechanical tests and survey results of different vegetation types in the study area; P ef is the effective rainfall, which is the amount of rainfall reaching the surface after vegetation interception and evaporation are subtracted from the total rainfall. T Represents soil permeability, calculated by multiplying the saturated hydraulic conductivity by the soil depth. A up represents the upslope drainage area, B Indicates the width of the flow boundary, sin Represents the head gradient.

[0295] In some embodiments, the parameter quantization unit 802 is further configured to:

[0296] The additional root-soil cohesion provided by plant roots is calculated by the following formula:

[0297] (25)

[0298] (26)

[0299] (27)

[0300] in, C root It is the additional cohesion provided by the reinforcement effect of plant roots; is the correction factor; It represents the average tensile strength per unit root cross-sectional area; and are shear distortion and internal friction angle, respectively; RAR Represents root area ratio; A r represents the root cross-sectional area; A rs Represents the rooted soil area.

[0301] In some embodiments, the parameter quantification unit 802 is further configured to determine the self-gravity load of the soil and vegetation by the following formula:

[0302] (28)

[0303] in, W veg is the gravity load of different vegetation types, W soli The weight of soil.

[0304] It should be noted that the slope stability assessment device considering the positive and negative effects of vegetation provided in the embodiment of the present invention belongs to the same technical concept as the slope stability assessment method considering the positive and negative effects of vegetation described previously, and has the same beneficial effects, which will not be repeated here.

[0305] An embodiment of the present invention provides a computer-readable storage medium having computer-readable instructions stored thereon. When the computer-readable instructions are executed by a processor of a computer, the computer executes a slope stability assessment method that considers the positive and negative effects of vegetation as described in various embodiments of the present invention.

[0306] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of the claims and specification of the present invention.

Claims

1. A slope stability assessment method considering the positive and negative effects of vegetation, characterized in that: The method comprises: Based on the infinite slope model, the slope stability model is constructed by comprehensively considering the positive and negative effects of vegetation; Quantification of key parameters of slope cover: Determine the temporal rainfall matrix sequence with different rainfall durations under different regression periods; Quantify the effective rainfall that actually reaches the ground based on the interception capacity of different vegetation types and evapotranspiration; Determine the terrain wetness index; Quantify the additional root-soil cohesion provided by the anchoring effect of plant roots; Determine the self-gravity loads of soil and vegetation; The constructed slope stability model is shown in formula (1): （1） in, F S is the slope safety factor, C soil +C rt It represents the root-soil cohesion. b represents the side width of the evaluation unit, W veg is the gravity load of different vegetation types, W soil represents the weight of soil, m represents the topographic wetness index ( TWI ), h represents the depth of the sliding surface, γ w represents the water density, β represents the slope angle, φ represents the internal friction angle of soil, T E Represents earthquake force.

2. The method according to claim 1, characterized in that The step of determining the time series rainfall matrix sequence with different rainfall durations under different regression periods specifically includes: The Gouber model is constructed to obtain the cumulative intensity-frequency-duration curve of rainfall events. The Gouber model cumulative distribution is determined based on formula (2): （2） in, η and λ The scale and location parameters representing the form of the Guber model, z Represents the cumulative rainfall within a certain rainfall period; The return period of a particular rainfall event is expressed as: （3a） The probability weighted moment method is used to determine η and λ : （3b） （4） in, b 0 and b 1 refers to the first and second overall PWM of the Guber model, respectively; for r >0th r samples, and the probability weighted moment is obtained by the following formula: （5） in, n represents the sample size, including the annual maximum value of a specific rainfall duration, variable j represents the ascending order of samples, Represents the sequence of annual maximum values ​​in ascending order; Integrating equations (3b)-(5), we use equations (6)-(7) to determine the scale and location parameters of the estimated Guber model in the form: （6） （7）。 3. The method according to claim 2, characterized in that The method of reducing the time scale of rainfall events by using a bounded random cascade model specifically includes: When the accumulated rainfall is determined, the rainfall intensity of each subunit during the decomposition period is calculated by the following formula: （8） in, k represents the number of descending steps, i Indicated in k Subinterval rainfall intensity in step-down decomposition, Q 0 is the rainfall depth, which means the cumulative rainfall depth in a specific regression period. W is the sequence of the decline coefficients, i.e., the ratio of the rainfall intensity of the parent interval transferred to the child interval during the decline period, p yes W Function to follow; It is characterized by the following formula W : （9） in, B ( a ) represents the Beta function, including a parameter that varies with time scale a , a greater than zero, a Depending on the size of the time scale considered, it is expressed as: （10） Among them, k The time scale of the basic unit after subdivision is t =Dr / 2 k express, k The value is 1,2,3,..., n , a 0 and H represents a constant parameter; The parameters are estimated by the following formula a : （11） Calculated by the following formula W : （12） in, Indicates that the time series is recorded at position j The rainfall depth at the time interval is Dr , Nr represents the total number of records in the considered time interval Dr. When zero or undefined, W is not included in the analysis.

4. The method according to claim 1, characterized in that: The quantification of the effective rainfall actually reaching the ground based on the interception capacity and evapotranspiration of the same vegetation type specifically includes: Potential evapotranspiration was calculated using the following formula: （13） in, ET p,t For the t Daily potential evapotranspiration, ET r,t For the t The reference evapotranspiration for the day, K is the crop coefficient, the effects of crop transpiration and soil evaporation are integrated into K middle; The reference evapotranspiration is calculated by the following formula: （14） in, Ra represents extraterrestrial radiation, T avg represents the average daily temperature, and TD represents the diurnal temperature range, defined as the difference between the daily maximum and minimum air temperatures; The maximum canopy storage capacity is calculated by the following formula: （15） in, LAI is the leaf area index; The leaf area index was calculated using the following formula: （16） in, LAI max is the maximum leaf area index, FPAR is the photosynthetic radiation fraction, FPAR max is the maximum photosynthetic radiation fraction; The photosynthetic radiation fraction is calculated by the following formula: （17） in, SR is the photosynthetic radiation term, which is equal to , SR max for Maximum SR value , SR min for SR Minimum value , NDVI is the normalized difference vegetation index, FPAR max and FPAR min The values ​​of are 0.95 and 0.001 respectively; Update canopy water storage based on the day's precipitation: （18） Among them, Scan t is the canopy water storage on day t, Scan t−1 is the canopy water storage on day t−1, P t is the precipitation on day t Effective precipitation is the water that cannot be stored in the tree canopy and is calculated using the following formula: （19） in, P t represents the effective precipitation on day t, Scan t represents the canopy water storage on day t; After calculating the effective precipitation, the canopy water storage is updated as: （20） The intercept is calculated by the following formula: （21） in, Int t (mm) indicates the t The water intercepted by the sky, ET r,t (mm) indicates the t Reference evapotranspiration for the day; Update canopy water storage by subtracting the interception: （22）。 5. The method according to claim 1, characterized in that The terrain humidity index is determined by formula (23)-(24): （23） （24） in, TWI is the ratio of the water level height above the sliding surface, k s-rs represents the saturated hydraulic conductivity of the root-soil complex, which was quantified based on mechanical tests and survey results of different vegetation types in the study area; P e is the effective rainfall, which is the amount of rainfall reaching the surface after vegetation interception and evaporation are subtracted from the total rainfall. T Represents soil permeability, calculated by multiplying the saturated hydraulic conductivity by the soil depth. A up represents the upslope drainage area, B Indicates the width of the flow boundary, sin θ Represents the head gradient.

6. The method according to claim 1, characterized in that The additional root-soil cohesion provided by the quantified plant root system specifically includes: The additional root-soil cohesion provided by plant roots is calculated by the following formula: (25) (26) (27) in, C root It is the additional cohesion provided by the reinforcement effect of plant roots; is the correction factor; It represents the average tensile strength per unit root cross-sectional area; and are shear distortion and internal friction angle, respectively; RAR represents the root area ratio; A r represents the root cross-sectional area; A rs Represents the rooted soil area.

7. The method according to claim 1, characterized in that The self-gravity load of soil and vegetation is determined by the following formula: （28） in, W veg is the gravity load of different vegetation types, W soil The weight of soil.

8. A device for evaluating slope stability taking into account the positive and negative effects of vegetation, characterized in that: The device comprises: A model building unit is configured to build a slope stability model based on an infinite slope model and taking into account the positive and negative effects of vegetation; The parameter quantization unit is configured to quantize the key parameters of the coverage slope: Determine the temporal rainfall matrix sequence with different rainfall durations under different regression periods; Quantify the effective rainfall that actually reaches the ground based on the interception capacity of different vegetation types and evapotranspiration; Determine the terrain wetness index; Quantify the additional root-soil cohesion provided by the anchoring effect of plant roots; Determine the self-gravity loads of soil and vegetation; The constructed slope stability model is shown in formula (1): （1） in, F S is the slope safety factor, C soil +C rt It represents the root-soil cohesion. b represents the side width of the evaluation unit, W veg Represents the external load weight of vegetation, W soil represents the weight of soil, m represents the topographic wetness index ( TWI ), h represents the depth of the sliding surface, γ w represents the water density, β represents the slope angle, φ represents the internal friction angle of soil, T E Represents earthquake force.

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