Ecological reconstruction of mixed forest shelterbelt in sand dune environment and monitoring method thereof

Abstract

This invention discloses a method for ecological reconstruction of mixed forest shelterbelts in sand dune environments, relating to the field of ecological restoration technology. The method includes: determining a quantitative soil index based on the organic matter content, aggregate structure coefficient, and water-holding capacity of the sand dunes to be reconstructed, and accordingly dividing the dunes into initial desertification, transitional desertification, and semi-stable soil-forming zones; determining the mixed tree types and the sand-fixing soil index changing over time in each zone; dynamically setting the mixed planting ratio, spatial configuration, planting time window, and planting density based on the current quantitative soil index, a preset wind erosion rate, and the coupling rate of the two indices at corresponding stages to maximize vegetation survival rate and biomass accumulation; and tracking the evolution trend of the quantitative soil index until the soilification degree in each zone reaches the target threshold and a continuous wind and sand interception corridor is formed. This invention significantly improves the accuracy of shelterbelt reconstruction, vegetation survival rate, and long-term stability of the sand dune ecosystem through quantitative zoning, dynamic coupled planting, and adaptive closed-loop intervention.

Description

Technical Field

[0001] This invention relates to the field of treatment technology, specifically to a method for ecological reconstruction and monitoring of mixed forest shelterbelts in sand dune environments. Background Technology

[0002] Currently, ecological reconstruction of dune protection zones relies heavily on empirical tree species combinations and fixed-cycle management, lacking quantitative assessment and dynamic adaptation of the degree of dune soilification evolution. Existing methods typically employ uniform planting density and static monitoring thresholds, failing to consider the stage-specific differences in soilification levels across different dune regions and the nonlinear evolution of vegetation sand-fixing efficiency over time. This leads to problems such as low survival rates, imbalanced community structure, and delayed intervention under abrupt changes in wind and sand erosion loads or extreme climatic conditions.

[0003] In addition, traditional monitoring methods mostly rely on manual sampling or a single sensor, resulting in insufficient data inversion accuracy and a lack of dynamic baseline correction mechanisms, making it difficult to achieve real-time closed-loop control of the protective belt reconstruction process.

[0004] Therefore, there is an urgent need for a method for ecological reconstruction and monitoring of mixed forest shelterbelts in dune environments that can quantify soil formation processes, dynamically couple vegetation growth and sand fixation efficiency, and have intelligent monitoring and early warning capabilities. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method for ecological reconstruction and monitoring of mixed forest shelterbelts in dune environments. This method can quantify soil formation processes, dynamically couple vegetation growth with sand fixation efficiency, and possess intelligent monitoring and early warning capabilities.

[0006] A first aspect of the present invention provides a method for ecological reconstruction of mixed forest shelterbelts in dune environments, comprising the following steps: S1. Quantify the soil index based on the organic matter content, aggregate structure coefficient, and water holding capacity of the sand dunes to be reconstructed; S2. Determine the first, second, and third regions of the sand dunes to be reconstructed based on the quantified soil index; the first region is the initial desertification region, the second region is the transitional desertification region, and the third region is the semi-stable soil formation region. S3. Determine the mixed tree types for planting protective belts in different areas, and determine the sand-fixing soil index of different mixed tree types over time under different quantitative soil indices. S4. Based on the current quantitative soil index of different regions, set the mixing ratio and spatial configuration of mixed tree types in the protection belt according to the preset wind and sand erosion rate. Based on the coupling rate of sand fixation soil index and quantitative soil index of different mixed tree types at the corresponding time stage, determine the planting time window and planting density under different quantitative soil indices, so as to maximize the survival rate and biomass accumulation rate of vegetation in each region under different sand fixation soil indices. S5. Obtain the evolution trend of the quantitative soil index of each region in each time period. When the actual quantitative soil index deviates from the preset value due to sand dune erosion in a local area, intervene in the mixed tree type of the local area until the soil fertility of the first, second and third regions all reach the preset target threshold, and the protective belt as a whole forms a continuous wind and sand interception corridor.

[0007] As a preferred method, the quantified soil index is determined through the following steps: Soil samples were collected from the top 0-30cm layer of the sand dunes to be reconstructed, and the organic matter content was determined. Aggregate structure coefficient , water holding coefficient ; The quantitative soil index is determined using the following formula: ; in, , and These are the weighting coefficients; The threshold ranges of the quantified soil index corresponding to the first region, the second region and the third region are represented as [0, 0.3], (0.3, 0.6] and (0.6, 1.0), respectively.

[0008] As a preferred method, the specific steps for determining the sand-fixing soil index in step S3 are as follows: The reconstruction time was divided into the initial planting stage, the middle canopy closure stage and the later steady-state stage, and a time evolution model of the sand-fixing soil index was constructed. The soil index for sand fixation in the initial planting stage is represented by root anchoring force and surface roughness; the soil index for sand fixation in the middle canopy closure stage is represented by canopy interception rate and litter accumulation; and the soil index for sand fixation in the later steady-state stage is represented by soil microbial activity and root exudate aggregation effect. When the fluctuation of the sand-fixing soil index in adjacent time periods exceeds the preset threshold, the proportion of mixed tree types in the protective belt is redistributed.

[0009] As a preferred method, the specific steps for determining the planting time window and planting density in S4 are as follows: Based on the local annual precipitation distribution curve, the ground temperature rise gradient, and the cross-matching of the dormancy and budding periods of the target mixed tree types, the planting time window is determined to avoid the active wind erosion season and the period of extreme high temperature. Based on the theoretical planting density calculated by back-calculating the mature crown projection area and effective competitive radius of the root system of the target mixed trees, an alternating zone is constructed on the windward side of the forest edge of the protective belt to form a wind shadow area, thereby weakening the tangential impact of the preset wind and sand erosion rate.

[0010] As a preferred approach, in step S4, when setting the mixing ratio and spatial configuration, a dynamic erosion flux attenuation model is introduced to calculate the net sand-fixing effect. When the actual monitored sand-fixing soil index is lower than the theoretical threshold for the same period, it is determined that the soil formation process is hindered, and auxiliary growth-promoting interventions are implemented. The auxiliary growth-promoting interventions include the deployment of micro-water collection pit arrays under the forest, artificial biological crust inoculation, or local terrain micro-leveling, until the actual sand-fixing effect is restored to within the theoretical safety line.

[0011] As a preferred approach, the preset target thresholds in S5 include a quantitative soil index compliance threshold and a protective belt spatial connectivity threshold. If forest gap formation or a rebound in soil erosion flux is detected in the third region during the intervention period, it is determined that the community ecological redundancy is insufficient. In this case, inter-age replanting is carried out, and salt-tolerant deep-rooted tree species and nitrogen-fixing shrubs are introduced to reconstruct the vertical stratification structure, so that the actual quantitative soil index converges to the preset target threshold.

[0012] As a preferred approach, the specific steps for intervening in the mixed tree type in the local area in step S5 are as follows: When tree species with high sand-fixing soil index exert excessive shading or root competition inhibition on tree species with low sand-fixing soil index, leading to a decline in community diversity index, targeted thinning, dynamic optimization of understory light transmittance, and construction of root zone micro-isolation zones should be implemented to maintain the niche differentiation of different mixed tree types at specific quantitative soil index stages and prevent the accumulation of system vulnerability caused by the succession of a single dominant species.

[0013] A second aspect of the present invention provides a method for monitoring the ecological reconstruction of mixed forests in a dune environment, comprising the following steps: M1. Multi-source sensor arrays are deployed in the first, second, and third regions according to the principles of gridding and gradient. M2. Real-time collection of quantitative soil index parameters, vegetation growth status parameters and wind and sand environmental load parameters in each region through the multi-source sensor array, inputting the collected data into the edge computing node for spatiotemporal alignment and noise filtering, and outputting a standardized monitoring index stream. M3. Based on the standardized monitoring index flow, the current sand-fixing soil index and the evolution value of the quantified soil index of each region are inverted in real time, and compared with the preset target thresholds for each time stage to calculate the deviation rate and evolution slope. M4. When the deviation rate or evolution slope exceeds the preset limit, a graded early warning signal is generated, and the control command is sent back to the ecological reconstruction execution terminal.

[0014] As a preferred embodiment, the deployment density of the multi-source sensor array in M1 is negatively correlated with the quantified soil index; The first area employs a high-density shallow grid layout to capture the critical point of surface sandstorms. The second area adopts a medium-density three-dimensional layout to balance the canopy interception effect and the transport of water in the middle soil layer; The third region employs a low-density deep profile layout to monitor the evolution of aggregate structure and groundwater recharge trends in the root penetration zone.

[0015] As a preferred approach, the instantaneous wind speed profile, sediment transport rate attenuation gradient, surface vegetation coverage, root biomass density, and soil organic carbon turnover rate are extracted from M2 as input feature vectors, input to a lightweight machine learning model calibrated with a historical reconstructed data training set, and output the real-time value of the sand-fixing soil index of each monitoring node. The preset target threshold in M3 is modified based on seasonal precipitation fluctuations, regional ground temperature changes, and vegetation phenology to avoid misjudgment under extreme weather conditions by using a fixed threshold. The graded early warning signals in M4 are divided into three levels: yellow, orange, and red. The yellow warning indicates that the local growth conditions have deteriorated and triggers a water replenishment command. The orange warning indicates that the sand fixation effect is lower than the erosion load and triggers a community structure adjustment command. The red warning indicates that the quantitative soil index has declined or the protective belt has broken and triggers an emergency engineering sand fixation intervention command.

[0016] Compared with the prior art, the present invention has the following advantages: This invention achieves precise classification of dune areas by constructing a spatiotemporal evolution model of quantitative soil index and sand-fixing soil index. Based on the coupling rate of the two, the planting time window and planting density are adaptively adjusted, significantly improving the survival rate and biomass accumulation rate of vegetation at different soilization stages. Through feedback strategies such as graded early warning, targeted thinning, and uneven-aged replanting, the vertical stratification and niche differentiation of the community are maintained, the succession of a single dominant species is blocked, and ultimately a continuous and stable wind and sand interception corridor and a self-sustaining soil microcycle are formed.

[0017] This invention introduces a dynamic erosion flux decay model to calculate the net sand-fixing effect in real time and set a theoretical safety line; when the process is blocked, it automatically triggers targeted growth-promoting measures such as micro-water collection and biological crust inoculation to avoid blind management and ensure that each area converges efficiently to the target soilization level.

[0018] This invention utilizes a gradient-deployed multi-source sensor array, combined with a lightweight machine learning model, to achieve real-time inversion of the sand-fixing soil index; the dynamic baseline correction mechanism effectively avoids interference from extreme weather, significantly improving the robustness of monitoring data and the accuracy of early warning. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In a first aspect, this embodiment provides a method for ecological reconstruction of mixed forest shelterbelts in dune environments, such as... Figure 1 As shown, it includes the following steps: S1. Quantify the soil index based on the organic matter content, aggregate structure coefficient, and water holding capacity of the sand dunes to be reconstructed; S2. Determine the first, second, and third regions of the sand dunes to be reconstructed based on the quantified soil index; the first region is the initial desertification region, the second region is the transitional desertification region, and the third region is the semi-stable soil formation region. S3. Determine the mixed tree types for planting protective belts in different areas, and determine the sand-fixing soil index of different mixed tree types over time under different quantitative soil indices. S4. Based on the current quantitative soil index of different regions, set the mixing ratio and spatial configuration of mixed tree types in the protection belt according to the preset wind and sand erosion rate. Based on the coupling rate of sand fixation soil index and quantitative soil index of different mixed tree types at the corresponding time stage, determine the planting time window and planting density under different quantitative soil indices, so as to maximize the survival rate and biomass accumulation rate of vegetation in each region under different sand fixation soil indices. S5. Obtain the evolution trend of the quantitative soil index of each region in each time period. When the actual quantitative soil index deviates from the preset value due to sand dune erosion in a local area, intervene in the mixed tree type of the local area until the soil fertility of the first, second and third regions all reach the preset target threshold, and the protective belt as a whole forms a continuous wind and sand interception corridor.

[0023] As a preferred method, the quantified soil index is determined through the following steps: Soil samples were collected from the top 0-30cm layer of the sand dunes to be reconstructed, and the organic matter content was determined. Aggregate structure coefficient , water holding coefficient ; The quantitative soil index is determined using the following formula: ; in, , and These are the weighting coefficients; The threshold ranges of the quantified soil index corresponding to the first region, the second region and the third region are represented as [0, 0.3], (0.3, 0.6] and (0.6, 1.0), respectively.

[0024] As a preferred method, the specific steps for determining the sand-fixing soil index in step S3 are as follows: The reconstruction time was divided into the initial planting stage, the middle canopy closure stage and the later steady-state stage, and a time evolution model of the sand-fixing soil index was constructed. The soil index for sand fixation in the initial planting stage is represented by root anchoring force and surface roughness; the soil index for sand fixation in the middle canopy closure stage is represented by canopy interception rate and litter accumulation; and the soil index for sand fixation in the later steady-state stage is represented by soil microbial activity and root exudate aggregation effect. When the fluctuation of the sand-fixing soil index in adjacent time periods exceeds the preset threshold, the proportion of mixed tree types in the protective belt is redistributed.

[0025] As a preferred method, the specific steps for determining the planting time window and planting density in S4 are as follows: Based on the local annual precipitation distribution curve, the ground temperature rise gradient, and the cross-matching of the dormancy and budding periods of the target mixed tree types, the planting time window is determined to avoid the active wind erosion season and the period of extreme high temperature. Based on the theoretical planting density calculated by back-calculating the mature crown projection area and effective competitive radius of the root system of the target mixed trees, an alternating zone is constructed on the windward side of the forest edge of the protective belt to form a wind shadow area, thereby weakening the tangential impact of the preset wind and sand erosion rate.

[0026] As a preferred approach, in step S4, when setting the mixing ratio and spatial configuration, a dynamic erosion flux attenuation model is introduced to calculate the net sand-fixing effect. When the actual monitored sand-fixing soil index is lower than the theoretical threshold for the same period, it is determined that the soil formation process is hindered, and auxiliary growth-promoting interventions are implemented. The auxiliary growth-promoting interventions include the deployment of micro-water collection pit arrays under the forest, artificial biological crust inoculation, or local terrain micro-leveling, until the actual sand-fixing effect is restored to within the theoretical safety line.

[0027] As a preferred approach, the preset target thresholds in S5 include a quantitative soil index compliance threshold and a protective belt spatial connectivity threshold. If forest gap formation or a rebound in soil erosion flux is detected in the third region during the intervention period, it is determined that the community ecological redundancy is insufficient. In this case, inter-age replanting is carried out, and salt-tolerant deep-rooted tree species and nitrogen-fixing shrubs are introduced to reconstruct the vertical stratification structure, so that the actual quantitative soil index converges to the preset target threshold.

[0028] As a preferred approach, the specific steps for intervening in the mixed tree type in the local area in step S5 are as follows: When tree species with high sand-fixing soil index exert excessive shading or root competition inhibition on tree species with low sand-fixing soil index, leading to a decline in community diversity index, targeted thinning, dynamic optimization of understory light transmittance, and construction of root zone micro-isolation zones should be implemented to maintain the niche differentiation of different mixed tree types at specific quantitative soil index stages and prevent the accumulation of system vulnerability caused by the succession of a single dominant species.

[0029] A second aspect of this embodiment provides a method for monitoring the ecological reconstruction of mixed forests in a dune environment, comprising the following steps: M1. Multi-source sensor arrays are deployed in the first, second, and third regions according to the principles of gridding and gradient. M2. Real-time collection of quantitative soil index parameters, vegetation growth status parameters and wind and sand environmental load parameters in each region through the multi-source sensor array, inputting the collected data into the edge computing node for spatiotemporal alignment and noise filtering, and outputting a standardized monitoring index stream. M3. Based on the standardized monitoring index flow, the current sand-fixing soil index and the evolution value of the quantified soil index of each region are inverted in real time, and compared with the preset target thresholds for each time stage to calculate the deviation rate and evolution slope. M4. When the deviation rate or evolution slope exceeds the preset limit, a graded early warning signal is generated, and the control command is sent back to the ecological reconstruction execution terminal.

[0030] As a preferred embodiment, the deployment density of the multi-source sensor array in M1 is negatively correlated with the quantified soil index; The first area employs a high-density shallow grid layout to capture the critical point of surface sandstorms. The second area adopts a medium-density three-dimensional layout to balance the canopy interception effect and the transport of water in the middle soil layer; The third region employs a low-density deep profile layout to monitor the evolution of aggregate structure and groundwater recharge trends in the root penetration zone.

[0031] As a preferred approach, the instantaneous wind speed profile, sediment transport rate attenuation gradient, surface vegetation coverage, root biomass density, and soil organic carbon turnover rate are extracted from M2 as input feature vectors, input to a lightweight machine learning model calibrated with a historical reconstructed data training set, and output the real-time value of the sand-fixing soil index of each monitoring node. The preset target threshold in M3 is modified based on seasonal precipitation fluctuations, regional ground temperature changes, and vegetation phenology to avoid misjudgment under extreme weather conditions by using a fixed threshold. The graded early warning signals in M4 are divided into three levels: yellow, orange, and red. The yellow warning indicates that the local growth conditions have deteriorated and triggers a water replenishment command. The orange warning indicates that the sand fixation effect is lower than the erosion load and triggers a community structure adjustment command. The red warning indicates that the quantitative soil index has declined or the protective belt has broken and triggers an emergency engineering sand fixation intervention command.

[0032] Specifically, each step is described in detail in this embodiment.

[0033] In this embodiment, step S1 is configured to determine a quantitative soil index based on the organic matter content, aggregate structure coefficient, and water-holding capacity of the dune to be reconstructed. Specifically, soil samples are collected in a 50m × 50m grid within the 0-30cm layer of the top layer of the dune to be reconstructed, and the organic matter content is determined using the laboratory standard drying method and the ring sampler method. (mass fraction %), water-stable aggregate structure coefficient greater than 0.25 mm (%) and field water holding capacity coefficient (m³ / m³); the soil index was then determined using the following formula: ; in, , and These are the normalized weight coefficients obtained by dimensionality reduction of the historical sand dune sample database from the same climate zone using Principal Component Analysis (PCA). The threshold ranges for the quantified soil index corresponding to the first, second, and third regions are represented as [0, 0.3], (0.3, 0.6], and (0.6, 1.0), respectively. Based on the real-time calculated quantified soil index values, the sand dunes to be reconstructed are automatically divided into initial desertification areas (first region), transitional desertification areas (second region), and semi-stable soil-forming areas (third region), and independent digital elevation models and soil property raster layers are generated for each region.

[0034] Steps S2 and S3 are configured to determine the mixed tree types for planting protective belts in different areas and to construct a time evolution model of the sand-fixing soil index. Specifically, the system pre-selects mixed tree type combinations based on regional climate zones and groundwater depth. For example, pioneer drought-resistant tree species such as Haloxylon ammodendron and Caragana korshinskii are planted in the first area, transitional shrubs such as Salix psammophila and Caragana korshinskii are planted in the second area, and deep-rooted trees such as Pinus sylvestris and Ulmus parvifolia are planted in the third area.

[0035] This embodiment establishes a sand-fixing soil index that varies with the reconstruction time for each type of tree. and its quantitative inversion path Anchoring force density measured by in-situ root penetration instrument Surface relief measured by laser roughness meter Canopy retention rate Accumulated amount of litter Soil microbial respiration rate and the effect of root exudates on the clustering of roots Weighted calculation, in this implementation The calculation method is as follows The weighted calculation method will not be elaborated here for the sake of simplicity. It should be noted that in this embodiment, the sand-fixing soil index for mixed tree types is calculated by the proportion of each type of tree and the inter-group effect value, as follows: ; in, The total number of mixed tree types planted within the protective belt; For the first The planting ratio of tree species in the protective belt (calculated by the designed number of trees or the percentage of the diameter at breast height during the survival period), and meeting the following requirements. ; For the first Tree-like trees during reconstruction time Independent sand-fixing soil index; This is the inter-group effect moderating coefficient, determined based on regional site conditions and historical succession data (usually ranging from 0.10 to 0.25). This represents the intergroup synergistic effect value.

[0036] The intergroup synergistic effect value The specific calculation method for quantitatively characterizing the complementary gains of different mixed tree types in spatial resource utilization, microenvironmental modification, and material cycling is as follows: ; in, The root system vertical and horizontal niche differentiation index is calculated by inverting the root distribution overlap obtained through the microroot window system. The microclimate improvement coefficient formed by the canopy interleaving is obtained by weighted normalization of the variance of temperature and humidity fluctuations under the canopy and the wind speed attenuation gradient. The ratio of decomposition rates of carbon and nitrogen compositions for the mixed decomposition of litter from different tree species; , , The weight coefficients are determined by multivariate stepwise regression analysis, and the sum of the three is 1.

[0037] When the mixed planting configuration produces a positive synergistic gain, the overall sand-fixing soil index is higher than the linear weighted value of a single tree species; when When the interspecific competition suppression is dominant, the system automatically triggers the mixing ratio redistribution mechanism in step S4, reducing the proportion of competing tree species and replanting niche-complementary tree species to maintain the optimal output of the sand-fixing soil conversion efficiency of the protective belt.

[0038] The reconstruction time was divided into the initial planting stage (0-2 years), the mid-term canopy closure stage (2-5 years), and the late steady-state stage (5 years and above). The sand-fixing soil index in the initial planting stage was mainly represented by root anchoring force and surface roughness. The sand-fixing soil index in the mid-term canopy closure stage was mainly represented by canopy interception rate and litter accumulation. The sand-fixing soil index in the late steady-state stage was mainly represented by soil microbial activity and root exudate aggregation.

[0039] The magnitude of the change in the sand-fixing soil index between adjacent time periods: When the preset threshold is exceeded, this embodiment redistributes the proportion of mixed tree types within the protective belt to reach the target value, and reduces the proportion of pioneer tree species according to the principle of complementary ecological niches. Upgraded the list of stress-resistant, deep-rooted tree species ,in After rounding and then calibrating by intergroup effect values, the calibrated sand-fixing soil index is determined to be within the preset threshold range, ensuring that the community structure evolves smoothly with the soilification process.

[0040] Step S4 is configured to set the mixing ratio and spatial configuration based on the current quantified soil index and preset wind erosion rate of each region, and to determine the planting time window and planting density based on the coupling rate. Specifically, the system first defines the ecological adaptation coupling rate. The calculation formula is as follows: , in and This represents the ideal sand fixation and soil improvement benchmark value for this tree species in the target ecological zone.

[0041] This embodiment is based on the local annual precipitation distribution curve, the ground temperature rise gradient (soil temperature in the 10cm soil layer is stable at ≥8℃ for 5 consecutive days), and the cross-matching of the dormancy and budding periods of the target mixed tree type, avoiding the active wind erosion season (usually March-May) and the extreme high temperature period (≥38℃). To correct the dynamic offset of the planting time window sky, As a regional climate-sensitive factor, the planting density is based on the mature crown projection area of ​​the target mixed trees. Effective competition radius with root system Inverse calculation of theoretical density Furthermore, a 5-8m wide herbaceous-shrub alternating zone was constructed on the windward side of the protective belt to form a wind shadow area, thereby weakening the tangential shear force of the preset wind and sand erosion rate and maximizing the survival rate and biomass accumulation rate of vegetation in each area under different sand-fixing soil indices.

[0042] Furthermore, in S4, a dynamic erosion flux attenuation model is introduced to calculate the net sand-fixing effect when setting the mixing ratio and spatial configuration. The model equation is as follows: ,in The local annual potential wind erosion flux under no vegetation cover (calibrated by wind tunnel test or BSNE sand collector). Let be the vegetation dynamic attenuation constant. The system sets the theoretical safety line as the net sand-fixing effect. When the actual monitored sand-fixing soil index is lower than the theoretical threshold for the same period, it is determined that the soil formation process is hindered, and auxiliary growth-promoting intervention is automatically implemented: a micro water collection pit array (volume 15L) is set up under the forest at a tree spacing of 2m, artificial cyanobacteria lichen biocrust is inoculated (inoculation amount 50g / m²), or local terrain micro-leveling is carried out (undulation height difference ≤10cm) until the actual sand-fixing effect is restored to within the theoretical safety line.

[0043] Step S5 is configured to acquire the evolution trend of quantitative soil indices at each time stage and implement dynamic intervention. Specifically, the preset target thresholds include the quantitative soil index achievement threshold (≥0.3 for the first region, ≥0.6 for the second region, and ≥0.85 for the third region) and the spatial connectivity threshold of the protective belt (canopy closure ≥0.7 and wind and sand interception corridor continuity index ≥0.9). If forest gap formation (single tree lodging rate >15%) or a rebound in soil and water loss flux (surface runoff sediment content >5g / L) is detected in the third region during the intervention period, it is determined that the community ecological redundancy is insufficient, and a multi-age replanting operation is performed: salt-tolerant deep-rooted tree species (such as Populus euphratica and Salix babylonica) and nitrogen-fixing shrubs (such as Amorpha fruticosa) are introduced and mixed at a 1:3 plant spacing to reconstruct the vertical stratification structure, so that the actual quantitative soil index converges to the preset target threshold. When tree species with high sand-fixing soil index cause excessive shading (understory light intensity < 20% of full sunshine) or root competition inhibition on tree species with low sand-fixing soil index, leading to a decline in the community's Shannon diversity index, targeted thinning (preserving target tree species spacing ≥ 1.5 times the crown width), dynamic optimization of understory light transmittance (replanting shade-tolerant herbs such as alfalfa), and construction of root zone micro-isolation zones (60cm deep impermeable membrane) are implemented to maintain the niche differentiation of different mixed tree types at specific quantitative soil index stages and prevent the accumulation of system vulnerability caused by the succession of a single dominant species.

[0044] In an embodiment of the monitoring method of the present invention, step M1 is configured to deploy a multi-source sensor array in the first, second, and third regions according to the principles of gridding and gradient. The multi-source sensor array includes soil temperature, humidity, and salinity probes, a wind erosion flux trap (BSNE type array), a canopy micro-meteorological instrument (integrated ultrasonic wind speed / infrared radiation / temperature and humidity), and a root growth impedance meter (micro-root window scanning system). Specifically, the deployment density is negatively correlated with the quantified soil index: the first region uses a high-density shallow grid deployment (5m × 5m spacing, probe depth 0-20cm) to capture the critical wind speed for surface sandstorms; the second region uses a medium-density three-dimensional deployment (10m × 10m spacing, vertical stratification 0-60cm) to balance the canopy interception effect and the transport of water in the middle soil layer; the third region uses a low-density deep profile deployment (20m × 20m spacing, depth 0-150cm) to monitor the evolution of aggregate structure and groundwater recharge trend in the root penetration zone.

[0045] The M2 step is configured to collect and quantify soil index-related parameters, vegetation growth status parameters, and wind and sand environmental load parameters in real time. The data is then input into edge computing nodes for spatiotemporal alignment and noise filtering, outputting a standardized monitoring index stream. Crucially, the specific method for inverting the current sand-fixing soil index in M2 is as follows: Instantaneous wind speed profile, sediment transport rate attenuation gradient, surface vegetation cover (inverted using UAV multispectral NDVI), root biomass density (converted using impedance gauging), and soil organic carbon turnover rate (measured using a soil respiration chamber) are extracted as input feature vectors and input into a LightGBM lightweight machine learning model calibrated using historical reconstructed data training set. This model structure contains 32 decision trees with a maximum depth of 6. Feature engineering employs Min-Max normalization and class coding. The training set consists of continuous monitoring data (sample size > 100,000 records) from 30 typical reconstructed plots in the same ecological zone over the past 5 years. The model inversion accuracy has been verified through 10-fold cross-validation. Root mean square error Output the real-time value of the sand-fixing soil index for each monitoring node.

[0046] The M3 step is configured to retrieve the current sand-fixing soil index and quantified soil index evolution values ​​of each region in real time based on standardized monitoring index flows, and compare them with a preset target threshold. Specifically, the preset target threshold adopts a dynamic baseline setting: in this embodiment, baseline drift correction is performed based on seasonal precipitation fluctuations (±15% deviation triggers baseline drift), regional ground temperature changes (cumulative accumulated temperature correction coefficient), and vegetation phenology (greening / leaf fall phase shift), and the deviation rate is calculated. With evolution slope This avoids misjudgments caused by fixed thresholds under extreme weather conditions.

[0047] The M step is configured to generate a graded early warning signal when the deviation rate or evolution slope exceeds a preset value, and to send control instructions back to the ecological reconstruction execution terminal. Specifically, the graded early warning signal is divided into three levels: yellow, orange, and red. A yellow warning indicates deterioration of local growth conditions (such as soil moisture content being below the wilting point for 7 consecutive days), triggering a water replenishment instruction (starting the drip irrigation system or expanding the collection pit); an orange warning indicates that the sand fixation effect is lower than the erosion load, triggering a community structure adjustment instruction (replanting pioneer tree species or adjusting the mixed planting ratio); a red warning indicates a decline in the quantitative soil index or a break in the protective belt (decline for 3 consecutive months and canopy connectivity < 0.5), triggering an emergency engineering sand fixation intervention instruction (deploying mechanical sand barriers, soil improvement, and artificial ecological reconstruction).

[0048] In a specific embodiment of the present invention, ecological reconstruction is performed on a typical crescent-shaped dune chain region: firstly, the first region is calculated through grid sampling. Second area Third Region The system automatically delineates zoning boundaries based on weighting coefficients. A soil index model for sand fixation is established (S). Initially, Haloxylon ammodendron is the primary planting species. When the migration rate exceeds a threshold in the third year, the system automatically increases the proportion of Salix psammophila by 18% and decreases the proportion of Pinus sylvestris by 10%. The coupling rate is calculated (S4), and the planting window is postponed by 5 days to avoid the spring drought. Planting density is also calculated, and a wide cross-belt is set up on the windward side to reduce tangential wind erosion. After introducing an erosion attenuation model, if the net sand fixation effect falls below the safety line in the fourth year, the system automatically instructs the deployment of micro-collection pits and inoculation with biological crusts to attenuate the sand within the theoretical envelope. Subsequently, a connectivity alarm is triggered in the third area due to localized forest gaps. Populus euphratica and Amorpha fruticosa of different ages are introduced for replanting, along with directional thinning to maintain niche differentiation. A synchronously operating monitoring array is deployed according to a gradient. Edge nodes input wind speed, NDVI, and root impedance data into the LightGBM model in real time for inversion. After dynamic baseline correction, an orange alert is triggered, and the cloud platform sends back adjustment instructions to optimize the mixed planting ratio. Ultimately, each region steadily converged to the target threshold (0.35 for the first region, 0.62 for the second region, and 0.88 for the third region), forming a continuous interception corridor in the protective belt, thus achieving the self-sustaining reconstruction of the dune ecosystem. This method, through quantitative index-driven, dynamic model coupling, and multi-source sensor closed-loop monitoring, completely overcomes the technical bottlenecks of traditional sand fixation afforestation, such as strong reliance on experience, delayed intervention, and fluctuating survival rates.

[0049] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent variations only. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed combinations. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0050] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0051] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for ecological reconstruction of mixed forest shelterbelts in sand dune environments, characterized in that, Includes the following steps: S1. Quantify the soil index based on the organic matter content, aggregate structure coefficient, and water holding capacity of the sand dunes to be reconstructed; S2. Determine the first, second, and third regions of the sand dunes to be reconstructed based on the quantified soil index; the first region is the initial desertification region, the second region is the transitional desertification region, and the third region is the semi-stable soil formation region. S3. Determine the mixed tree types for planting protective belts in different areas, and determine the sand-fixing soil index of different mixed tree types over time under different quantitative soil indices. S4. Based on the current quantitative soil index of different regions, set the mixing ratio and spatial configuration of mixed tree types in the protection belt according to the preset wind and sand erosion rate. Based on the coupling rate of sand fixation soil index and quantitative soil index of different mixed tree types at the corresponding time stage, determine the planting time window and planting density under different quantitative soil indices, so as to maximize the survival rate and biomass accumulation rate of vegetation in each region under different sand fixation soil indices. S5. Obtain the evolution trend of the quantitative soil index of each region in each time period. When the actual quantitative soil index deviates from the preset value due to sand dune erosion in a local area, intervene in the mixed tree type of the local area until the soil fertility of the first, second and third regions all reach the preset target threshold, and the protective belt as a whole forms a continuous wind and sand interception corridor.

2. The method for ecological reconstruction of mixed forest shelterbelts in dune environments according to claim 1, characterized in that, The quantitative soil index is determined through the following steps: Soil samples were collected from the top 0-30cm layer of the sand dunes to be reconstructed, and the organic matter content was determined. Aggregate structure coefficient , water holding coefficient ; The quantitative soil index is determined using the following formula: ； in, , and These are the weighting coefficients; The threshold ranges of the quantified soil index corresponding to the first region, the second region and the third region are represented as [0, 0.3], (0.3, 0.6] and (0.6, 1.0), respectively.

3. The method for ecological reconstruction of mixed forest shelterbelts in dune environments according to claim 2, characterized in that, The specific steps for determining the sand-fixing soil index in S3 are as follows: The reconstruction time was divided into the initial planting stage, the middle canopy closure stage and the later steady-state stage, and a time evolution model of the sand-fixing soil index was constructed. The soil index for sand fixation in the initial planting stage is represented by root anchoring force and surface roughness; the soil index for sand fixation in the middle canopy closure stage is represented by canopy interception rate and litter accumulation; and the soil index for sand fixation in the later steady-state stage is represented by soil microbial activity and root exudate aggregation effect. When the fluctuation of the sand-fixing soil index in adjacent time periods exceeds the preset threshold, the proportion of mixed tree types in the protective belt is redistributed.

4. The method for ecological reconstruction of mixed forest shelterbelts in sand dune environments according to claim 3, characterized in that, The specific steps for determining the planting time window and planting density in S4 are as follows: Based on the local annual precipitation distribution curve, the ground temperature rise gradient, and the cross-matching of the dormancy and budding periods of the target mixed tree types, the planting time window is determined to avoid the active wind erosion season and the period of extreme high temperature. Based on the theoretical planting density calculated by back-calculating the mature crown projection area and effective competitive radius of the root system of the target mixed trees, an alternating zone is constructed on the windward side of the forest edge of the protective belt to form a wind shadow area, thereby weakening the tangential impact of the preset wind and sand erosion rate.

5. The method for ecological reconstruction of mixed forest shelterbelts in sand dune environments according to claim 4, characterized in that, In S4, when setting the mixing ratio and spatial configuration, a dynamic erosion flux attenuation model is introduced to calculate the net sand fixation effect. When the actual monitored sand-fixing soil index is lower than the theoretical threshold for the same period, it is determined that the soil formation process is hindered, and auxiliary growth-promoting interventions are implemented. The auxiliary growth-promoting interventions include the deployment of micro-water collection pit arrays under the forest, artificial biological crust inoculation, or local terrain micro-leveling, until the actual sand-fixing effect is restored to within the theoretical safety line.

6. The method for ecological reconstruction of mixed forest shelterbelts in sand dune environments according to claim 5, characterized in that, The preset target thresholds in S5 include the quantitative soil index compliance threshold and the protective belt spatial connectivity threshold. If forest gap formation or a rebound in soil erosion flux is detected in the third region during the intervention period, it is determined that the community ecological redundancy is insufficient. In this case, inter-age replanting is carried out, and salt-tolerant deep-rooted tree species and nitrogen-fixing shrubs are introduced to reconstruct the vertical stratification structure, so that the actual quantitative soil index converges to the preset target threshold.

7. The method for ecological reconstruction of mixed forest shelterbelts in sand dune environments according to claim 6, characterized in that, When intervening in the mixed tree type of the local area in S5, the specific steps are as follows: When tree species with high sand-fixing soil index exert excessive shading or root competition inhibition on tree species with low sand-fixing soil index, leading to a decline in community diversity index, targeted thinning, dynamic optimization of understory light transmittance, and construction of root zone micro-isolation zones should be implemented to maintain the niche differentiation of different mixed tree types at specific quantitative soil index stages and prevent the accumulation of system vulnerability caused by the succession of a single dominant species.

8. A method for monitoring the ecological reconstruction of mixed forests in a sand dune environment, characterized in that, The method applied to any one of claims 1 to 7 includes the following steps: M1. Multi-source sensor arrays are deployed in the first, second, and third regions according to the principles of gridding and gradient. M2. Real-time collection of quantitative soil index parameters, vegetation growth status parameters and wind and sand environmental load parameters in each region through the multi-source sensor array, inputting the collected data into the edge computing node for spatiotemporal alignment and noise filtering, and outputting a standardized monitoring index stream. M3. Based on the standardized monitoring index flow, the current sand-fixing soil index and the evolution value of the quantified soil index of each region are inverted in real time, and compared with the preset target thresholds for each time stage to calculate the deviation rate and evolution slope. M4. When the deviation rate or evolution slope exceeds the preset limit, a graded early warning signal is generated, and the control command is sent back to the ecological reconstruction execution terminal.

9. A method for monitoring the ecological reconstruction of mixed forests in a dune environment according to claim 8, characterized in that, The deployment density of the multi-source sensor array in M1 is negatively correlated with the quantitative soil index. The first area employs a high-density shallow grid layout to capture the critical point of surface sandstorms. The second area adopts a medium-density three-dimensional layout to balance the canopy interception effect and the transport of water in the middle soil layer; The third region employs a low-density deep profile layout to monitor the evolution of aggregate structure and groundwater recharge trends in the root penetration zone.

10. A method for monitoring the ecological reconstruction of mixed forests in a dune environment according to claim 8, characterized in that, The instantaneous wind speed profile, sediment transport rate attenuation gradient, surface vegetation coverage, root biomass density, and soil organic carbon turnover rate are extracted from M2 as input feature vectors. These vectors are then input into a lightweight machine learning model calibrated with historical reconstructed data training set, and the real-time values ​​of the sand-fixing soil index of each monitoring node are output. The preset target threshold in M3 is modified based on seasonal precipitation fluctuations, regional ground temperature changes, and vegetation phenology to avoid misjudgment under extreme weather conditions by using a fixed threshold. The graded early warning signals in M4 are divided into three levels: yellow, orange, and red. The yellow warning indicates that the local growth conditions have deteriorated and triggers a water replenishment command. The orange warning indicates that the sand fixation effect is lower than the erosion load and triggers a community structure adjustment command. The red warning indicates that the quantitative soil index has declined or the protective belt has broken and triggers an emergency engineering sand fixation intervention command.

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