A method for monitoring forest hydrological changes

Abstract

This invention relates to the field of hydrological monitoring, and more particularly to a method for monitoring forest hydrological changes. The method includes: determining a snowfall trend characterization value based on the snow interception amount in the target forest area's canopy, and determining whether to perform environmental disturbance analysis based on the snowfall trend characterization value and its duration; in the environmental disturbance analysis, determining whether to conduct characteristic disturbance analysis for the target forest area based on environmental disturbance parameters; in the characteristic disturbance analysis, determining the structural snow-slip resistance value based on the stand canopy closure and tree species resilience of the target trees, and determining the structural category of the target trees based on the structural snow-slip resistance value; determining the fluctuation disturbance area determination method based on the number of target trees of different structural categories to obtain the fluctuation disturbance area; and determining whether to issue a monitoring accuracy warning for the fluctuation disturbance area based on the number of fluctuation disturbance areas and whether environmental disturbance analysis has been performed. This invention improves the accuracy of forest hydrological change monitoring.

Description

Technical Field

[0001] This invention relates to the field of hydrological monitoring, and more particularly to a method for monitoring forest hydrological changes. Background Technology

[0002] Forest hydrological monitoring is a crucial foundation for assessing the water conservation function of forest ecosystems, providing early warnings of snowmelt floods, and guiding forestry management. Especially in cold-temperate and temperate forest regions, the interaction between winter snow cover and the forest canopy profoundly affects watershed water balance, spring runoff processes, and stand safety. Currently, existing forest hydrological monitoring technologies suffer from the following shortcomings: most methods focus on summer precipitation redistribution, lacking continuous monitoring and analysis of dynamic processes such as winter canopy snow interception, snow accumulation, and snow shedding. Most technologies rely on static interception rate empirical values, failing to determine whether a dedicated environmental disturbance analysis is needed based on dynamic characteristics such as actual snowfall intensity and duration, leading to wasted monitoring resources or omissions of critical processes. Therefore, there is an urgent need in this field for a forest hydrological change monitoring method and device that can integrate snowfall dynamics, environmental disturbances, and tree structural characteristics, and possess the ability to identify and provide early warnings of fluctuating disturbance areas.

[0003] Chinese Patent Publication No. CN120337732A discloses a method for inverting forest snow albedo, including selecting the Forest Snow Two-Dimensional Reflectance Model (SFBR2) as the forward model; extracting forest snow cover areas using land cover type products and snow cover products; randomly generating multiple sample points within the forest snow cover area and extracting input features from all sample points; inverting the target variable using an optimization algorithm based on the input feature samples and the Forest Snow Two-Dimensional Reflectance Model (SFBR2); constructing a training dataset by combining the input features and the target variable; training a random forest model based on the constructed dataset and optimizing it using a grid search algorithm; and building the model on the GEE platform. A long-term time-series image set containing input features is used to apply an optimized random forest model to accurately invert the black and white albedo of forest snow cover. However, the above technical solution has the following problems: the static albedo estimation method based on remote sensing images and statistical models does not consider the real-time correlation between canopy snow interception and changes in snowfall intensity and duration during the dynamic process of forest snow cover; in scenarios of continuous snowfall and environmental disturbance, it lacks the comprehensive dynamic analysis capability that integrates environmental factors and canopy characteristics, causing the monitoring strategy for the snowfall process to be unable to adaptively adjust according to the actual conditions of each forest area, thus affecting the accuracy and timeliness of forest hydrological monitoring. Summary of the Invention

[0004] This invention provides a method for monitoring forest hydrological changes, which overcomes the shortcomings of existing technologies in continuously monitoring and analyzing dynamic processes such as winter canopy snow interception, snow accumulation, and snow shedding, and the inability to conduct adaptive monitoring based on actual scenarios, resulting in low monitoring accuracy.

[0005] To achieve the above objectives, the present invention provides a method for monitoring forest hydrological changes, comprising: The snowfall trend characterization value is determined based on the snowfall interception amount in the target forest area, and the environmental disturbance analysis is determined based on the snowfall trend characterization value and the duration. In environmental disturbance analysis, environmental disturbance parameters are determined based on the influencing factors corresponding to the target forest area, and based on the environmental disturbance parameters, it is determined whether to conduct characteristic disturbance analysis for the target forest area. In the feature interference analysis, the structural snow-slip resistance value is determined based on the stand canopy closure and tree species bending resistance of the target trees, and the structural category of the target trees is determined based on the structural snow-slip resistance value. The method for determining the fluctuation interference area is based on the number of target trees of different structural categories, so as to obtain the fluctuation interference area; Based on the number of areas affected by fluctuations and whether environmental interference analysis is performed, it is determined whether to issue early warnings for monitoring accuracy in areas affected by fluctuations.

[0006] Furthermore, the amount of snow intercepted by the forest canopy is determined by comprehensively considering both the amount of snowfall outside the forest and the amount of snow penetrating into the forest in the target forest area; Based on the snowfall trend characterization value and the condition that any parameter in the duration exceeds the constraint range, the environmental interference analysis is determined.

[0007] Further, environmental disturbance analysis includes: Obtain the influencing factors corresponding to the target forest area, including the environmental reference wind speed, snow layer reference moisture content, and environmental reference temperature. The influence coefficient of each influence factor is determined based on the degree of persistent disturbance corresponding to each influence factor. Environmental disturbance parameters are determined based on a combination of impact coefficients and impact factors. Based on the condition that the environmental interference parameter is greater than the preset environmental interference parameter, characteristic interference analysis is determined for the target forest area; The environmental disturbance parameters and the influencing factors are positively correlated.

[0008] Furthermore, the structural resistance to snowfall is determined based on the stand canopy closure and tree species bending resistance of the target trees; The target tree's structural category includes a high snow-resistant structural category with a structural snow-slip resistance value greater than a preset structural snow-slip resistance value, and a low snow-resistant structural category with a structural snow-slip resistance value less than or equal to a preset structural snow-slip resistance value.

[0009] Furthermore, based on the condition that the proportion of high snow-resistant structure categories is greater than the preset category proportion, the determination of the fluctuation interference area is based on snowfall reference characteristics.

[0010] Furthermore, based on the condition that the proportion of low snow-resistant structure categories is greater than the preset category proportion, the determination of the fluctuation interference area is based on tree reference features.

[0011] Furthermore, the determination of the fluctuation interference area based on snowfall reference characteristics includes: Monitor the abrupt changes in data from each snowfall acquisition device; For any snowfall acquisition device, the monitoring time of the snowfall acquisition device whose corresponding data monitoring mutation degree is greater than the preset data monitoring mutation degree is recorded as an interference time; Based on the condition that the interference density at a given time is greater than the preset interference density at a given time, the monitoring area corresponding to the snowfall acquisition device is determined to be the fluctuation interference area.

[0012] Furthermore, the determination of the fluctuation interference region based on tree reference features includes: Based on the condition that the canopy superposition influence of the uniform sub-region is greater than the preset canopy superposition influence, the uniform sub-region is determined to be a fluctuation interference region.

[0013] Furthermore, the benchmark for determining the canopy stacking impact based on the target stacked trees includes: Determine the target superimposed trees based on the constraint of easy-to-fall superposition; For cases where the number of target superimposed trees is greater than the preset number of target superimposed trees, the acquisition benchmark is determined to be the distribution of the target superimposed trees; For cases where the number of target superimposed trees is less than or equal to the preset number of target superimposed trees, the acquisition benchmark is determined to be based on the distribution of target trees.

[0014] Furthermore, for the condition that the number of fluctuating interference areas is greater than the preset number of fluctuating interference areas, a monitoring accuracy warning is determined for the fluctuating interference areas; If the number of fluctuating interference areas is less than or equal to the preset number of fluctuating interference areas and there is no need to perform environmental interference analysis, it is determined that there is no need to perform monitoring accuracy warning.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: In this invention, the snowfall trend characterization value is determined based on the amount of snow intercepted by the forest canopy, and the environmental interference analysis is determined based on this characterization value and its duration. Analysis is performed on abnormal or continuous snowfall situations, avoiding resource consumption caused by redundant calculations and effectively balancing monitoring accuracy and system energy consumption.

[0016] Furthermore, this invention introduces environmental reference wind speed, snow layer reference moisture content, and environmental reference temperature as influencing factors, and adaptively determines the influence coefficient based on the continuous disturbance degree corresponding to each factor, thereby obtaining environmental disturbance parameters. Through environmental disturbance parameters, it is determined whether further characteristic disturbance analysis is needed, thus overcoming the problem of poor data analysis accuracy caused by single index threshold analysis in traditional methods.

[0017] Furthermore, the structural snow-slip resistance value is determined based on the stand canopy closure and tree species resilience of the target trees, and the trees are classified into high-snow-resistant structural categories and low-snow-resistant structural categories. Based on the proportion of different structural categories, a corresponding method for determining the area of ​​fluctuation interference is selected, avoiding the problem of poor applicability of monitoring methods caused by fixed analysis methods, thereby improving the accuracy of hydrological change monitoring in this invention.

[0018] Furthermore, this invention can accurately identify areas of fluctuation interference based on the density of interference at any given time or the area ratio of easily disturbed sub-regions. By comprehensively analyzing the number of areas of fluctuation interference and whether environmental interference analysis has been performed, it determines whether a monitoring accuracy warning has been triggered, alerting the user that the current monitoring data may be affected by local anomalies, thus avoiding hydrological assessment decisions based on distorted data and significantly improving the reliability of monitoring results. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the forest hydrological change monitoring method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating how to determine whether to perform feature interference analysis on a target forest area based on environmental interference parameters, according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating how the structural category of a target tree is determined based on its structural snow-resistant sliding value, according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the method for determining the fluctuation interference area based on the number of target trees of different structural categories, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0023] Please see Figure 1 As shown, it is a schematic diagram of a forest hydrological change monitoring method according to an embodiment of the present invention. The present invention provides a forest hydrological change monitoring method, including: The snowfall trend characterization value is determined based on the snowfall interception amount in the target forest area, and the environmental disturbance analysis is determined based on the snowfall trend characterization value and the duration. In environmental disturbance analysis, environmental disturbance parameters are determined based on the influencing factors and their corresponding influence coefficients corresponding to the target forest area, and based on these parameters, it is determined whether to conduct characteristic disturbance analysis for the target forest area. In the feature interference analysis, the structural snow-slip resistance value is determined based on the stand canopy closure and tree species bending resistance of the target trees, and the structural category of the target trees is determined based on the structural snow-slip resistance value. The method for determining the fluctuation interference area is based on the number of target trees of different structural categories, so as to obtain the fluctuation interference area; Based on the number of areas affected by fluctuations and whether environmental interference analysis is performed, it is determined whether to issue early warnings for monitoring accuracy in areas affected by fluctuations.

[0024] In this embodiment of the invention, environmental interference analysis is adaptively triggered by dual constraints of snowfall trend characterization value and duration, which improves the efficiency of monitoring resource utilization. The influence coefficient is determined based on the continuous interference degree corresponding to the influence factor to comprehensively determine the intensity of environmental interference. When the environmental interference parameter is greater than the preset environmental interference parameter, feature interference analysis is triggered. The target trees are effectively identified as having high snow resistance and low snow resistance structure categories by using the structural snow resistance and slippage value. Based on the proportion of different structure categories, the determination of the fluctuation interference area is selected based on snowfall reference features or tree reference features. Adaptive decision-making for monitoring accuracy warning is made based on the number of fluctuation interference areas and whether environmental interference analysis is performed. This avoids the inability of existing technologies to dynamically adjust the monitoring strategy according to the actual state of each forest area in the context of continuous snowfall and environmental disturbance, thereby improving the accuracy and timeliness of forest hydrological change monitoring.

[0025] Specifically, the amount of snow intercepted by the forest canopy is determined by combining the amount of snowfall outside the forest and the amount of snow penetrating into the forest in the target forest area; Based on the snowfall trend characterization value and the condition that any parameter in the duration exceeds the constraint range, the environmental interference analysis is determined.

[0026] In this embodiment of the invention, the process of obtaining the snowfall outside the forest and the snow penetration inside the forest includes: setting up a snowfall acquisition device outside the forest in an open area outside the target forest area. The snowfall acquisition device is a weighing snow gauge, installed on a horizontal support at a certain height above the ground; setting up several snowfall acquisition devices inside the forest in the target forest area in a grid-like layout. Each snowfall acquisition device is located below the canopy layer, and its installation height is consistent with that of the snowfall acquisition device outside the forest. The horizontal spacing between adjacent devices is determined according to the stand density of the target forest area, and the horizontal spacing is negatively correlated with the stand density; the collected data is transmitted to the forest area monitoring center server via wireless communication; the server arranges and generates a continuous time series of snowfall outside the forest and snow penetration inside the forest in the order of collection time.

[0027] In this embodiment of the invention, the process of obtaining snow interception by the canopy includes: snow interception by the canopy reflects the snow load borne by the canopy surface and the degree to which the canopy redistributes snowfall space. A larger snow interception by the canopy indicates that the canopy intercepts and retains more snow on the canopy surface, resulting in a greater snow load borne by the canopy, a higher probability of snow slippage and a higher potential impact, and simultaneously, less snow penetrates the canopy to reach the ground. Environmental interference has a more significant relative impact on measurement accuracy. In this embodiment, snow interception by the canopy is the average reading of all snowfall acquisition devices outside the forest at the same time, minus the average reading of snowfall penetration from all snowfall acquisition devices inside the forest. Snow interception by the canopy and snowfall penetration within the forest are negatively correlated; a larger snow interception by the canopy results in less snowfall penetration within the forest, indicating a higher efficiency of snowfall interception by the canopy.

[0028] In this embodiment of the invention, the process of obtaining the snowfall trend characterization value includes: the snowfall trend characterization value is used to determine the degree of change in canopy-intercepted snowfall within a unit time. A larger snowfall trend characterization value indicates a greater range of change in canopy-intercepted snowfall within a unit time, higher instability of the canopy snow load, more severe fluctuations in the dynamic load borne by the canopy branches, and an increased probability of snow slippage and local instability of the canopy structure. In this embodiment, multiple canopy-intercepted snowfall data points continuously collected in the most recent snowfall analysis cycle are used, and linear fitting is performed using the least squares method. The absolute value of the slope of the fitted straight line is taken as the snowfall trend characterization value. The duration is the cumulative duration during which the canopy-intercepted snowfall within the most recent snowfall analysis cycle is greater than a preset canopy-intercepted snowfall. The longer the duration, the longer the canopy layer continuously bears a high snow load, and the more significant the interference of the cumulative effect of elastic deformation of canopy branches and leaves and the change in the internal cohesion of the snow layer on the measurement of penetrating snowfall. Simultaneously, the dynamic effect of environmental factors on intercepted snowfall increases with the duration. In this embodiment, the canopy snow interception amount within the historical snowfall cycles of the target forest area is collected. After removing outliers, the average value is calculated and used as the preset canopy snow interception amount. The longer the snowfall analysis cycle, the larger the amount of data collected and analyzed in a single session, resulting in higher analysis accuracy. Therefore, the higher the user's requirement for analysis accuracy, the longer the snowfall analysis cycle. However, it is worth noting that an excessively long snowfall analysis cycle can lead to a decrease in the timeliness of data analysis. The above points are easily understood by those skilled in the art and will not be elaborated further.

[0029] In this embodiment of the invention, the process of obtaining the constraint range includes: the constraint range is used to determine the safety judgment boundary that each monitoring parameter does not exceed the normal fluctuation range and does not need to trigger subsequent analysis. Each monitoring parameter has a different constraint range. For any given monitoring parameter, the corresponding constraint range is pre-calibrated through simulation. The simulation comprehensively considers the dynamic changes in canopy interception under different combinations of snowfall intensity, wind speed, and ambient temperature. Using the accuracy of snow penetration measurement and system resource consumption as comprehensive evaluation indicators, the minimum critical value of the monitoring parameter that meets the evaluation requirements is selected as the upper limit, and the lower limit is taken as the physical zero value, thus forming a closed interval, denoted as the constraint range of the monitoring parameter. When any parameter, such as the real-time calculated snowfall trend characterization value or duration, exceeds the upper limit of its corresponding constraint range, it is determined that the triggering condition is met and subsequent environmental interference analysis is performed. This ensures that the adaptive triggering of the monitoring strategy avoids both excessive sensitivity leading to resource waste and excessive sluggishness leading to missed interference judgments.

[0030] Specifically, environmental disturbance analysis includes: Obtain the influencing factors corresponding to the target forest area, including the environmental reference wind speed, snow layer reference moisture content, and environmental reference temperature. The influence coefficient of each influence factor is determined based on the degree of persistent disturbance corresponding to each influence factor. Environmental disturbance parameters are determined based on a combination of impact coefficients and impact factors. Based on the condition that the environmental interference parameter is greater than the preset environmental interference parameter, characteristic interference analysis is determined for the target forest area; The environmental disturbance parameters and the influencing factors are positively correlated.

[0031] In this embodiment of the invention, the process of obtaining the influencing factors of the target forest area includes: setting up environmental monitoring stations within the target forest area, each including a wind speed sensor, a snow moisture content sensor, and an ambient temperature sensor; the wind speed sensor is an ultrasonic anemometer, installed at a certain height above the canopy layer, to collect ambient reference wind speed data; the snow moisture content sensor is a frequency domain reflectance method snow water equivalent sensor, buried at a depth in the middle of the snow layer within the forest, to collect snow reference moisture content data; the ambient temperature sensor is a platinum resistance temperature sensor, installed at a certain height above the ground, to collect ambient reference temperature data; the sampling frequency of each sensor is consistent with that of the snow volume acquisition device, and the collected data is synchronously transmitted to the forest area monitoring center server.

[0032] In this embodiment of the invention, the process of obtaining the persistent disturbance degree includes: the influence coefficient is adaptively determined based on the dynamic cumulative disturbance degree of each influencing factor within the duration. The persistent disturbance degree is used to determine the cumulative magnitude and volatility of a single environmental factor deviating from its historical stable baseline within the duration. The greater the persistent disturbance degree, the stronger the persistent disturbance of the environmental factor on the stability of canopy intercepted snow and snow penetration measurements in the current period, and the greater its contribution to the final environmental disturbance parameter should be. In this embodiment, for each influencing factor, a continuous monitoring sequence of the influencing factor is extracted over the entire duration, the cumulative offset of the sequence relative to the moving average of the influencing factor is calculated, and the cumulative offset is normalized and mapped to a closed interval of zero to one to obtain the persistent disturbance degree of the influencing factor. The process of obtaining the influence coefficient includes normalizing the persistent disturbance degree of each influencing factor by a proportional transformation, using the proportion of the persistent disturbance degree of each factor to the sum of the persistent disturbance degrees of all factors as the influence coefficient corresponding to each factor, so that the sum of the weights of all influencing factors is normalized to one, to ensure that the environmental disturbance parameter obtained by subsequent summation has a unified dimension and comparability.

[0033] In this embodiment of the invention, the process of obtaining environmental interference parameters includes: environmental interference parameters are used to determine the interference intensity of external environmental factors on the measurement accuracy of snow interception and snow penetration. A larger environmental interference parameter indicates a more significant impact of current environmental conditions on the reliability of snowfall monitoring data, and a greater likelihood that the snow penetration measurement value will deviate from the true value. Therefore, it is more necessary to initiate characteristic interference analysis to identify specific affected areas. In this embodiment, the real-time monitoring values ​​of each influencing factor are processed using a linear normalization method to obtain the normalized characterization value of each factor. Then, the normalized characterization value of each factor is weighted and summed with the corresponding influence coefficient. The environmental interference parameter is positively correlated with each influencing factor; that is, an increase in the monitoring value of any influencing factor or an increase in the corresponding sustained interference will lead to a corresponding increase in the environmental interference parameter.

[0034] In this embodiment of the invention, the preset environmental interference parameter serves as a critical value for determining whether to perform feature interference analysis. The setting process includes: setting the parameter based on historical monitoring data of the target forest area and forestry engineering practice experience; collecting environmental reference wind speed, snow layer reference moisture content, and environmental reference temperature monitoring data within the historical snowfall cycle of the target forest area; combining the actual statistical results of the snow penetration measurement error within the corresponding time period; and having those skilled in the art determine the value of the preset environmental interference parameter based on the correspondence between the acceptable error threshold and the environmental interference parameter. The longer the historical snowfall cycle, the larger the amount of data used to analyze the preset value, and the higher the reliability of the analysis. Therefore, the higher the reliability requirement for the preset value results, the longer the historical snowfall cycle. However, it is worth noting that an excessively long historical snowfall cycle may cause the collected data to span periods of significant changes in forest stand structure. The above are all content easily understood by those skilled in the art and will not be elaborated further.

[0035] Specifically, the structural snow-slip resistance value is determined based on the stand canopy closure and tree species bending resistance of the target trees. The target tree's structural category includes a high snow-resistant structural category with a structural snow-slip resistance value greater than a preset structural snow-slip resistance value, and a low snow-resistant structural category with a structural snow-slip resistance value less than or equal to a preset structural snow-slip resistance value.

[0036] In this embodiment of the invention, the process of obtaining the stand canopy closure includes: the stand canopy closure is used to determine the proportion of snowfall intercepted by the canopy layer in the target forest area and the degree of canopy space filling. The higher the stand canopy closure, the larger the physical interception area of ​​snowfall by the canopy layer, the more snowfall a unit area of ​​canopy can bear, and the higher the potential risk of snow runoff. In this embodiment, a canopy analyzer is used to obtain canopy hemispherical images in the target forest area according to gridded sampling points. Sky visibility is calculated through image processing, and the average value of the difference between the baseline value and the sky visibility is the stand canopy closure.

[0037] In this embodiment of the invention, the process of obtaining the tree species' flexural strength includes: the tree species' flexural strength is used to determine the ability of the target tree's branches and trunks to resist bending and fracture under snow load. A higher tree species' flexural strength indicates higher toughness and strength of the tree branches and trunks, and a lower probability of branch bending or snow slippage under the same snow load. In this embodiment, the trunk diameter at breast height (DBH), branch base diameter, and wood elastic modulus of the target tree are obtained. The critical bending stress of the branches and trunks is calculated using the bending stress formula in mechanics of materials. The critical bending stress is calculated as the ratio of the numerator to the standard reference stress, which is the tree species' flexural strength.

[0038] In this embodiment of the invention, the process of obtaining the structural snow-slip resistance value includes: the structural snow-slip resistance value is used to determine the structural tendency of target trees to slip under canopy snow load. A larger structural snow-slip resistance value indicates higher structural stability of the tree under snow load and a lower tendency to slip; conversely, a smaller structural snow-slip resistance value indicates that the tree's structural characteristics are more likely to induce snow slip. The structural snow-slip resistance value is the product of the stand canopy closure and the tree species' resilience. The structural snow-slip resistance value is positively correlated with both stand canopy closure and tree species resilience. Therefore, the higher the sensitivity requirement for structural stability classification, the larger the preset structural snow-slip resistance value should be. In this embodiment, actual structural snow-slip resistance values ​​are collected within historical snowfall cycles in the target forest area. After removing outliers, the average value of the structural snow-slip resistance values ​​is calculated, and this average value is used as the preset structural snow-slip resistance value. Methods for removing outliers include, but are not limited to, the 3σ principle.

[0039] Specifically, based on the condition that the proportion of high snow-resistant structure categories is greater than the proportion of preset categories, the determination of the fluctuation interference area is based on snowfall reference characteristics.

[0040] In this embodiment of the invention, the proportion of high snow-resistant structure categories is used to characterize the proportion of trees with high structural resistance to snowfall in the target forest area among all target trees. When the proportion of high snow-resistant structure categories is large, it indicates that most trees in the forest area have strong resistance to snowfall. Snowfall events are mainly caused by the characteristics of snowfall itself, such as snowfall intensity and uneven snowfall distribution. In this case, a judgment method based on snowfall reference characteristics is adopted, which identifies fluctuating interference areas by analyzing the abrupt change characteristics of snowfall data.

[0041] Specifically, based on the condition that the proportion of low snow-resistant structure categories is greater than the proportion of preset categories, the determination of the fluctuation interference area is based on tree reference characteristics.

[0042] In this embodiment of the invention, the proportion of low snow-resistant structure categories is used to characterize the proportion of trees with low structural snow-fall resistance values ​​among all target trees in the target forest area. When the proportion of low snow-resistant structure categories is large, it indicates that the structural characteristics of most trees in the forest area are inherently prone to inducing snowfall, and snowfall events are mainly dominated by the differences in the tree's own structure. In this case, a judgment method based on tree reference characteristics is adopted, and the fluctuation interference area is identified by analyzing the canopy superposition influence. In this embodiment, the preset category proportion is set based on the empirical principle of majority decision-making, without relying on external prior parameters, and has adaptive judgment capability under different forest areas and different tree species compositions.

[0043] Specifically, the determination of fluctuation interference areas based on snowfall reference characteristics includes: Monitor the abrupt changes in data from each snowfall acquisition device; For any snowfall acquisition device, the monitoring time of the snowfall acquisition device whose corresponding data monitoring mutation degree is greater than the preset data monitoring mutation degree is recorded as an interference time; Based on the condition that the interference density at a given time is greater than the preset interference density at a given time, the monitoring area corresponding to the snowfall acquisition device is determined to be the fluctuation interference area.

[0044] In this embodiment of the invention, the process of determining the fluctuation interference area based on snowfall reference characteristics includes: acquiring continuous snowfall monitoring sequences from each forest snowfall acquisition device over a sustained period; calculating the absolute value of the difference between the current monitoring time and the previous monitoring value as the data monitoring mutation degree; defining a circular influence range with a preset radius centered on each forest snowfall acquisition device, and using the circular influence range of each device as the corresponding monitoring area; the boundary of each monitoring area is adaptively determined based on the gridded coordinates of the forest snowfall acquisition devices and the forest stand density. When the forest stand density is higher, the distance between adjacent devices is smaller, and the monitoring area coverage is correspondingly reduced to avoid overlap; when the forest stand density is lower, the monitoring area coverage is correspondingly expanded to ensure the integrity of spatial coverage; the preset radius is determined based on the average forest stand density of the target forest area and is negatively correlated with the average forest stand density; the preset radius is determined based on the distance between adjacent devices, and the preset radius is positively correlated with the distance between adjacent devices. A larger data monitoring mutation degree indicates a drastic jump in the snowfall monitoring value at that moment, which is very likely caused by local snow sliding and hitting the snowfall acquisition device. When the density of interference moments exceeds the preset density of interference moments, it indicates that snowfall events are occurring frequently within the monitoring area where the device is located, and the monitoring area corresponding to the device is identified as a fluctuating interference area. In this embodiment, the data monitoring mutation rate of the forest snowfall acquisition device within the historical snowfall cycle of the target forest area is collected. After removing outliers, the average value is calculated, which is the preset data monitoring mutation rate. Taking each monitoring area as a unit, the number of interference moments within the historical snowfall cycle of the target forest area is collected. After removing outliers, the average number of interference moments per unit time is calculated, which is the preset number of interference moments.

[0045] Specifically, the determination of fluctuation interference areas based on tree reference features includes: Based on the condition that the canopy superposition influence of the uniform sub-region is greater than the preset canopy superposition influence, the uniform sub-region is determined to be a fluctuation interference region.

[0046] Specifically, the benchmark for determining the canopy stacking impact based on the target stacked trees includes: Determine the target superimposed trees based on the constraint of easy-to-fall superposition; For cases where the number of target superimposed trees is greater than the preset number of target superimposed trees, the acquisition benchmark is determined to be the distribution of the target superimposed trees; For cases where the number of target superimposed trees is less than or equal to the preset number of target superimposed trees, the acquisition benchmark is determined to be based on the distribution of target trees.

[0047] In this embodiment of the invention, the process of determining the fluctuation interference area based on tree reference features includes: uniformly dividing the target forest area into several uniform sub-regions according to a preset spatial scale, wherein the preset spatial scale is determined based on the stand density of the target forest area, and the preset spatial scale is negatively correlated with the stand density; determining target superimposed trees based on the easy-fall superimposed constraint condition, wherein the target superimposed trees are target trees of low snow resistance structure category whose tree height is greater than the height of neighboring trees and whose superimposed influence distance is less than the preset superimposed influence distance; and calculating the canopy superimposed influence degree based on the distribution of the target superimposed trees for the condition that the number of target superimposed trees is greater than the preset number of target superimposed trees, including: for each uniform sub-region, obtaining the structural snow resistance slippage value, height prominence rate, and distance to the nearest forest snow volume acquisition device of each target superimposed tree in the region; multiplying the structural snow resistance slippage value of each target superimposed tree by the height prominence rate, and then dividing by the sum of its distance to the nearest device and the preset distance benchmark value to obtain the single tree superimposed influence value of each tree; and calculating the average value of all single tree superimposed influence values, wherein the average value is the canopy superimposed influence degree of the uniform sub-region. Among them, the height prominence rate is the ratio of the tree height to the average height of trees in the neighborhood; the preset distance benchmark value is determined based on the distance between adjacent devices, and the preset distance benchmark value and the distance between adjacent devices are positively correlated.

[0048] In this embodiment of the invention, for the condition that the number of target superimposed trees is less than or equal to the preset number of target superimposed trees, the process of obtaining the canopy superposition influence degree includes: for each uniform sub-region, statistically analyzing the structural snow-fall resistance values ​​of all target trees in that region, and summing the structural snow-fall resistance values ​​of each tree according to their spatial distribution within the sub-region to obtain the canopy superposition influence degree. The canopy superposition influence degree is used to characterize the comprehensive influence of the composite effect of the structural characteristics of multiple trees within a uniform sub-region on the tendency of snowfall in the region. The larger the canopy superposition influence degree, the higher the comprehensive instability risk of the tree structure within that sub-region, the greater the probability of cascading snowfalls, and the more significant the interference of snowfalls on the measurement data of the snowfall acquisition device. Therefore, this uniform sub-region is determined as a fluctuating interference region. In this embodiment, the canopy superposition influence degree within the historical snowfall cycle of the target forest area is collected, outliers are removed, and the average value is calculated. This average value is used as the preset canopy superposition influence degree.

[0049] Specifically, when the number of fluctuating interference areas exceeds the preset number of fluctuating interference areas, a monitoring accuracy warning is issued for the fluctuating interference areas. If the number of fluctuating interference areas is less than or equal to the preset number of fluctuating interference areas and there is no need to perform environmental interference analysis, it is determined that there is no need to perform monitoring accuracy warning.

[0050] In this embodiment of the invention, the process of monitoring accuracy warning for the fluctuation interference area includes: determining when to issue a monitoring accuracy warning, generating a warning signal, the warning signal including spatial distribution markers of the fluctuation interference area, the interference start time corresponding to each fluctuation interference area, and the interference duration; pushing the warning signal to the visualization unit of the forest area monitoring center server, whereby the visualization unit highlights and flashes the fluctuation interference area in a three-dimensional visual model, and sends a warning notification message to a preset management terminal. In this embodiment, the number of fluctuation interference areas within historical snowfall cycles in the target forest area is collected, outliers are removed, and the average value is calculated, which is then used as the preset number of fluctuation interference areas.

[0051] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring forest hydrological changes, characterized in that, include: The snowfall trend characterization value is determined based on the snowfall interception amount in the target forest area, and whether to perform environmental disturbance analysis is determined based on the dual constraints of the snowfall trend characterization value and the duration. In environmental disturbance analysis, environmental disturbance parameters are determined based on the influencing factors corresponding to the target forest area, and based on the environmental disturbance parameters, it is determined whether to conduct characteristic disturbance analysis for the target forest area. In the feature interference analysis, the structural snow-slip resistance value is determined based on the stand canopy closure and tree species bending resistance of the target trees, and the structural category of the target trees is determined based on the structural snow-slip resistance value. The method for determining the fluctuation interference area is based on the number of target trees of different structural categories, so as to obtain the fluctuation interference area; Based on the number of areas affected by fluctuations and whether environmental interference analysis is performed, it is determined whether to issue early warnings for monitoring accuracy in areas affected by fluctuations.

2. The forest hydrological change monitoring method according to claim 1, characterized in that, The amount of snow intercepted by the forest canopy is determined by combining the amount of snowfall outside the forest and the amount of snow penetrating into the forest in the target forest area. Based on the snowfall trend characterization value and the condition that any parameter in the duration exceeds the constraint range, the environmental interference analysis is determined.

3. The forest hydrological change monitoring method according to claim 2, characterized in that, Environmental interference analysis, including: Obtain the influencing factors corresponding to the target forest area, including the environmental reference wind speed, snow layer reference moisture content, and environmental reference temperature. The influence coefficient of each influence factor is determined based on the degree of persistent disturbance corresponding to each influence factor. Environmental disturbance parameters are determined based on a combination of impact coefficients and impact factors. Based on the condition that the environmental interference parameter is greater than the preset environmental interference parameter, characteristic interference analysis is determined for the target forest area; The environmental disturbance parameters and the influencing factors are positively correlated.

4. The forest hydrological change monitoring method according to claim 3, characterized in that, The structural snow-slip resistance value is determined based on the stand canopy closure and tree species bending resistance of the target trees. The target tree's structural category includes a high snow-resistant structural category with a structural snow-slip resistance value greater than a preset structural snow-slip resistance value, and a low snow-resistant structural category with a structural snow-slip resistance value less than or equal to a preset structural snow-slip resistance value.

5. The forest hydrological change monitoring method according to claim 4, characterized in that, Based on the condition that the proportion of high snow-resistant structure categories is greater than the preset category proportion, the determination of the fluctuation interference area is based on snowfall reference characteristics.

6. The forest hydrological change monitoring method according to claim 4, characterized in that, Based on the condition that the proportion of low snow-resistant structure categories is greater than the preset category proportion, the determination of the fluctuation interference area is based on tree reference features.

7. The forest hydrological change monitoring method according to claim 5, characterized in that, Determining the area of ​​fluctuation interference based on snowfall reference characteristics includes: Monitor the abrupt changes in data from each snowfall acquisition device; For any snowfall acquisition device, the monitoring time of the snowfall acquisition device whose corresponding data monitoring mutation degree is greater than the preset data monitoring mutation degree is recorded as an interference time; Based on the condition that the interference density at a given time is greater than the preset interference density at a given time, the monitoring area corresponding to the snowfall acquisition device is determined to be the fluctuation interference area.

8. The forest hydrological change monitoring method according to claim 6, characterized in that, Determining the area of ​​fluctuation interference based on tree reference features includes: Based on the condition that the canopy superposition influence of the uniform sub-region is greater than the preset canopy superposition influence, the uniform sub-region is determined to be a fluctuation interference region.

9. The forest hydrological change monitoring method according to claim 8, characterized in that, The benchmark for determining the canopy stacking impact based on the target stacked trees includes: Determine the target superimposed trees based on the constraint of easy-to-fall superposition; For cases where the number of target superimposed trees is greater than the preset number of target superimposed trees, the acquisition benchmark is determined to be the distribution of the target superimposed trees; For cases where the number of target superimposed trees is less than or equal to the preset number of target superimposed trees, the acquisition benchmark is determined to be based on the distribution of target trees.

10. The forest hydrological change monitoring method according to claim 1, characterized in that, When the number of areas affected by fluctuations exceeds the preset number of areas affected by fluctuations, a monitoring accuracy warning is determined for the target forest area. If the number of areas affected by fluctuations is less than or equal to the preset number of areas affected by fluctuations and there is no need to perform environmental interference analysis, then it is determined that no monitoring accuracy warning is required.

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