A method and system for reconstructing and improving the function of a degraded shelterbelt network

Abstract

The application discloses a kind of degradation shelter forest network structure reconfiguration and function promotion method and system, belong to new technology and innovation and entrepreneurship service technical field, including the following steps: build sky-ground cooperative monitoring system, obtain remote sensing data, carry out shelter forest network degradation diagnosis based on remote sensing data, obtain the degradation state of shelter forest network, when diagnosis state is serious degradation, trigger shelter forest network structure reconfiguration, otherwise keep existing state;If execute shelter forest network structure reconfiguration, then based on remote sensing data, risk area determination is carried out to the region to be monitored, and according to the risk area determination result, adjust stand structure data and internet of things sensor network data appropriately;Acquire adjusted stand structure data and internet of things sensor network data, and jointly remote sensing data are used as sky-ground cooperative monitoring system data, solve the problem that repair effect is not accurate due to serious mismatch between static planning scheme and dynamic forest status.

Description

Technical Field

[0001] This invention relates to the field of new technologies and innovation and entrepreneurship service technology, and in particular to a method and system for reconstructing and enhancing the function of degraded shelterbelt networks. Background Technology

[0002] With the intensification of global climate change and human activities, protective forest networks in ecologically fragile areas face severe degradation challenges, seriously threatening regional ecological security and sustainable economic and social development. For example, in the Ulan Buh Desert region along the Yellow River's "U-shaped bend," the climate is arid, wind erosion is frequent, groundwater levels are rapidly declining, and protective forest networks along the Yellow River's desert borders and oasis farmland are dying off due to over-maturity. Therefore, resolutely winning the battle against desertification in the Yellow River's "U-shaped bend" is crucial. Currently, the fragile ecological environment in this region exerts continuous pressure on trees throughout their growth cycle, leading to instability in artificial ecosystems, slow vegetation growth, and even large-scale death. This presents unprecedented challenges to the reconstruction and maintenance of degraded protective forest networks. Therefore, it is necessary to strengthen the protection of protective forest networks, dynamically restructure them, and prevent functional degradation. While methods for vegetation optimization and stability exist, optimizing vegetation construction through various environmental constraints, they do not achieve structural degradation diagnosis and restructuring of the forest network, thus hindering overall sustainable management.

[0003] The existing shelterbelt network structure reconstruction and functional enhancement are based on soil moisture conditions and soil salinity. By adjusting tree species restoration patterns, tree species types and densities, precise grading and differentiated targeted transformation of plantation degradation are achieved, optimizing forest stand structure and enhancing the protective function of plantations.

[0004] For example, Chinese invention patent CN120338357A discloses a method for efficient optimization and control of water regulation coupled with dynamic assessment of black soil farmland quality. This method includes: introducing the impact of water processes on black soil erosion to achieve accurate assessment of soil loss; combining key constraints such as soil erosion and salinization to propose an optimized calculation method, scientifically deciding on the optimal irrigation water volume and irrigation method, ensuring that the black soil region meets crop water requirements while minimizing the risk of soil degradation; and integrating key indicators from multiple dimensions such as soil erosion, salinization, soil nutrients, and soil structure to propose an index model (SRI) for quantifying the restoration effect of black soil, quantitatively assessing soil health status and restoration effect.

[0005] For example, Chinese invention patent CN119784260B discloses a method for graded and targeted transformation of low-quality poplar plantations, which includes: deploying multiple sensors at different locations in the plantation area to construct a sensor network for data collection, and using a fusion algorithm to process the data collected by the sensors; constructing a plantation degradation level classification model, extracting index data and sub-index data from the fused data, constructing a constraint function in the constraint layer for constraint, and constructing a stability index function and degradation index function under the optimal weight coefficient; in the target layer, realizing the classification of plantation degradation levels by setting a non-degradation index threshold; constructing a comprehensive transformation objective function, and carrying out targeted transformation according to the classified plantation degradation levels, thereby achieving different degrees of targeted transformation at different levels by adjusting the adjustment coefficients under different levels.

[0006] The above-mentioned technology has at least the following technical problems: Current technologies for restoring degraded shelterbelt networks rely on static adjustments to address the discrepancy between the current state and the ideal stand structure. During the monitoring phase of restoration, data collection is limited to scattered, single-function sensors with low frequency. By the time problems are detected, significant restoration challenges have already arisen, neglecting the dynamic and complex environmental stresses that vegetation endures throughout its long growth cycle. Therefore, the severe mismatch between static planning and the dynamic state of the forest results in restoration measures that are detached from the actual environment, including soil moisture, vegetation carrying capacity, and salt stress, leading to inaccurate restoration outcomes. Summary of the Invention

[0007] To address the technical problem of inaccurate restoration effects due to a severe mismatch between static planning schemes and the current state of forest vegetation, resulting in restoration measures that are detached from the actual environment, soil moisture and vegetation carrying capacity, salt stress, and other factors, this invention provides a method and system for reconstructing and enhancing the function of degraded shelterbelt networks. The technical solution is as follows: On the one hand, a method for reconstructing and enhancing the function of degraded shelterbelt networks is provided. This method includes: establishing a sky-ground collaborative monitoring system to obtain remote sensing data; diagnosing the degradation of the shelterbelt network based on the remote sensing data to obtain its degradation status; triggering structural reconstruction of the shelterbelt network when the diagnosed status is severe degradation, otherwise maintaining the existing status; if structural reconstruction is to be performed, determining the risk zone of the monitored area based on the remote sensing data to obtain the risk zone determination result; adjusting the stand structure data and IoT sensor network data according to the risk zone determination result; acquiring the adjusted stand structure data and IoT sensor network data, and combining them with the remote sensing data as data for the sky-ground collaborative monitoring system; comprehensively processing this data to obtain a vegetation regulation parameter set, which includes at least the target canopy vegetation density and the target vegetation restoration mode; constructing an integrated soil and water distribution map based on the sky-ground collaborative monitoring system data; dividing the area into regions; obtaining the site type of each region, including hydrological zoning type, land use zoning type, and soil zoning type; and combining this with the vegetation regulation parameter set to obtain a shelterbelt network structural reconstruction signal, and then executing the shelterbelt network structural reconstruction scheme.

[0008] On the other hand, a system for reconstructing and enhancing the function of degraded shelterbelt networks is provided. This system includes: a degradation status analysis module, a risk response acquisition and control module, a vegetation control parameter generation module, and a region division and structural reconstruction module. The degradation status analysis module is used to establish a sky-ground collaborative monitoring system to obtain remote sensing data. Based on the remote sensing data, it performs a degradation diagnosis of the shelterbelt network to obtain its degradation status. When the diagnosed status is severe degradation, it triggers structural reconstruction of the shelterbelt network; otherwise, it maintains the existing status. The risk response acquisition and control module is used to determine the risk zone of the monitored area based on remote sensing data if shelterbelt network structural reconstruction is to be performed, obtain the risk zone determination result, and adjust the forest stand structure parameters according to the risk zone determination result. According to data from the Internet of Things (IoT) sensor network; the vegetation regulation parameter generation module is used to acquire adjusted forest stand structure data and IoT sensor network data, and combine them with remote sensing data as data for the sky-ground collaborative monitoring system, and comprehensively process them to obtain a vegetation regulation parameter set, which includes at least the target canopy vegetation density and the target vegetation restoration mode; the regional division and structural reconstruction module is used to construct an integrated soil and water distribution map based on the sky-ground collaborative monitoring system data, divide the region, obtain each divided region, obtain the site type of each divided region, including hydrological zoning type, land use zoning type and soil zoning type, and combine the vegetation regulation parameter set to obtain the shelterbelt network structure reconstruction signal, and execute the shelterbelt network structure reconstruction scheme.

[0009] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention provides a method for reconstructing and enhancing the function of degraded shelterbelt networks. It determines the degradation of the shelterbelt network based on remote sensing data. If the degradation is deemed severe, the shelterbelt network structure is reconstructed. A set of vegetation control parameters and an integrated soil and water distribution map are obtained through data processing from a sky-ground collaborative monitoring system. Based on the integrated soil and water distribution map, regions are divided to obtain the site types for each region. Combined with the vegetation control parameter set, a shelterbelt network structure reconstruction signal is obtained, thus completing the structural reconstruction of the degraded shelterbelt network. This effectively solves the problem in existing technologies where adjustment plans are formulated solely based on the static structural gap between the current state and the ideal stand, neglecting the limitations imposed by factors such as soil moisture and salinity during the plant's growth cycle, resulting in inaccurate restoration effects.

[0010] 2. This invention determines risk zones based on vegetation canopy porosity and leaf area index, thereby obtaining the vegetation degradation level of the shelterbelt network under environmental stress. Based on this, the vegetation restoration mode and density in the stand structure data are dynamically adjusted, thus achieving intelligent optimization of data acquisition accuracy. This ensures that high-precision, high-frequency structural data can be obtained in high-risk areas with severe vegetation degradation and environmental stress, achieving accurate data acquisition. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for reconstructing and enhancing the function of a degraded shelterbelt network, as provided in this application embodiment; Figure 2 A flowchart illustrating the adaptive reconstruction process of a degraded shelterbelt network structure and function enhancement method provided in this application embodiment; Figure 3 A schematic diagram illustrating the restoration of degraded shelterbelts in a specific region; Figure 4 A schematic diagram of a system for reconstructing and enhancing the function of a degraded shelterbelt network, provided in an embodiment of this application; Figure 5 A classification map showing the restoration targets and vegetation types in the region. Detailed Implementation

[0013] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0014] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0015] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0016] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0017] Example 1, Figure 1 The diagram shows a flowchart of a method for reconstructing and enhancing the function of a degraded shelterbelt network according to an embodiment of this application. The method includes the following steps: establishing a sky-ground collaborative monitoring system to obtain remote sensing data; performing a shelterbelt network degradation diagnosis based on the remote sensing data to obtain the degradation status of the shelterbelt network; triggering shelterbelt network structure reconstruction when the diagnosed status is severe degradation, otherwise maintaining the existing status; if shelterbelt network structure reconstruction is performed, determining the risk zone of the monitored area based on the remote sensing data to obtain the risk zone determination result; adjusting the forest stand structure data and IoT sensor network data according to the risk zone determination result; and obtaining the adjusted data. The integrated forest stand structure data and IoT sensor network data, along with remote sensing data, are used as data for a sky-ground collaborative monitoring system. This data is then processed to obtain a vegetation regulation parameter set, which includes at least the target canopy vegetation density and the target vegetation restoration mode. Based on the sky-ground collaborative monitoring system data, an integrated soil and water distribution map is constructed, and regional divisions are performed. The site types for each region are obtained, including hydrological zoning, land use zoning, and soil zoning. Combined with the vegetation regulation parameter set, a shelterbelt network structure reconstruction signal is obtained, and a shelterbelt network structure reconstruction scheme is implemented.

[0018] In this embodiment, as Figure 2 As shown, Figure 2This application provides an embodiment of an adaptive reconstruction flowchart for a method to restructure and enhance the function of a degraded shelterbelt network. The flowchart describes how a multi-source data acquisition system, built using a sky-ground collaborative monitoring framework, determines the degradation status of the shelterbelt network and initiates the reconstruction process only when the degradation is severe, minimizing unnecessary intervention. Risk levels are assigned to target areas based on remote sensing data, and data from drones and IoT sensors are adaptively adjusted according to these risk levels to ensure data accuracy. The acquired multi-source data is fused and analyzed to generate a set of vegetation control parameters, including target canopy vegetation density and vegetation restoration patterns. Site zoning and type identification are performed using high-precision soil and water distribution maps, resulting in a reconstruction scheme that includes vertical structure configuration and tree species selection. This achieves a transformation in shelterbelt network management from macro-monitoring to precise policy implementation, from static planning to dynamic adaptation, and from experience-based judgment to data-driven decision-making.

[0019] A sky-ground collaborative monitoring system is constructed, which systematically integrates multi-source data from satellites, drones, and ground-based internet sensor networks, and inputs it into a unified data processing and decision-making platform for fusion analysis. The final output is a key system for guiding the restoration of degraded shelterbelt networks.

[0020] The data from the sky-ground collaborative monitoring system includes: remote sensing data of the shelterbelt network obtained through satellite remote sensing, aerial data obtained by drones, and data collected by the Internet of Things sensor network on the ground.

[0021] First, the acquired remote sensing images are preprocessed, including at least radiometric calibration, atmospheric correction, geometric correction, and image stitching and cropping, to eliminate sensor errors and atmospheric effects, ensuring the image data has a unified spatial reference and radiometric scale. Second, based on the preprocessed remote sensing images, vegetation index information is constructed using band calculation methods. This vegetation index includes at least the Normalized Difference Vegetation Index (NDVI) and spectral indices related to vegetation structure. An initial leaf area index estimate reflecting vegetation growth is obtained through multi-band combination calculations. Simultaneously, stratified sampling is conducted within the study area according to the vertical stratification and horizontal distribution characteristics of the vegetation. Representative plots are selected to establish several observation points. At each observation point, a canopy analyzer is used to measure the hemispherical field of view of the canopy, acquiring transmitted light data. The measured ground leaf area index value is then calculated based on the principle of radiative transfer. Finally, the measured ground leaf area index values ​​at each observation point are compared with the corresponding spatial reference and radiometric scale. Spatial matching of remotely sensed vegetation index values ​​at different locations was performed. A functional relationship model between the remotely sensed vegetation index and the measured leaf area index (LAI) on the ground was established using regression analysis. This functional relationship model can be a linear or nonlinear model, and the model with the highest goodness of fit was selected as the LAI inversion model. Based on the LAI inversion model, pixel-by-pixel inversion was performed on the remotely sensed images of the study area to generate a regional-scale LAI distribution map. Furthermore, based on the canopy radiation transmission theory, the canopy transmittance was derived from hemispherical images or from the LAI inversion results to calculate the vegetation canopy porosity distribution map. The vegetation canopy porosity is the proportion of sky pixels to the total number of pixels in a unit observation area. The LAI and canopy porosity are negatively correlated; that is, under the same conditions, the larger the LAI, the higher the canopy shading degree and the smaller the canopy porosity; conversely, the smaller the LAI, the sparser the canopy structure and the larger the canopy porosity.

[0022] Aerial data is acquired through drones. The lidar on the drones can penetrate part of the canopy to obtain the three-dimensional structure of the forest stand (tree height, crown width, canopy closure) with centimeter-level accuracy. Multispectral cameras can obtain vegetation physiological status information with higher spatial resolution, which can be mutually verified and supplemented with satellite data.

[0023] Ground-based IoT sensor networks collect continuous, real-time process information, while deployed soil moisture and salinity sensors directly measure habitat conditions in the plant root zone to facilitate the selection of target vegetation restoration modes. Through spatiotemporal registration and multi-source data fusion technologies, an organic whole is formed, achieving a comprehensive upgrade in monitoring dimensions from two-dimensional to three-dimensional, monitoring frequency from periodic to continuous, and monitoring content from superficial phenomena to underlying mechanisms. This fundamentally avoids decision-making biases caused by incomplete or delayed data.

[0024] Furthermore, the degradation status of the shelterbelt network is obtained through the following method: acquiring remote sensing data (near-infrared surface reflectance, red light band surface reflectance, and short-wave infrared band surface reflectance), processing it to obtain vegetation canopy porosity and leaf area index; based on this, the degradation index of the shelterbelt network is calculated, and compared with the degradation threshold of the shelterbelt network to obtain the degradation status of the shelterbelt network. When the degradation index of the shelterbelt network is greater than the degradation threshold of the shelterbelt network, the degradation status of the shelterbelt network is determined to be severely degraded; otherwise, the degradation status of the shelterbelt network is determined to be normal.

[0025] In this embodiment, vegetation canopy porosity, leaf area index, and various preset reference values ​​are obtained, and a protective forest network degradation index is obtained through processing. Specifically, the reference values ​​for vegetation canopy porosity and leaf area index are compared with the values ​​for the current time period, and then divided by their respective reference values. The results are then averaged to obtain the protective forest network degradation index. A higher protective forest network degradation index indicates a more severe degree of degradation of the protective forest network.

[0026] Accurate analysis of the degradation status of shelterbelt networks is the starting point and prerequisite for the entire restoration process. This enables "restore on demand," avoiding indiscriminate resource allocation in a "one-size-fits-all" manner. Traditional restoration measures often indiscriminately restore large areas, ignoring the heterogeneity of degradation levels within these areas. This leads to wasted resources on still-healthy stands while insufficient investment in truly degraded areas. However, by conducting preliminary analysis of the degradation status of shelterbelt networks, it is ensured that only areas whose ecological functions have fallen below the maintenance threshold and genuinely require human intervention will trigger the subsequent series of reconstruction processes. This not only significantly improves the investment efficiency of restoration projects but also reduces unnecessary interference with areas that still have natural restoration potential.

[0027] Furthermore, risk zone determination is performed using the following method: based on vegetation canopy porosity and leaf area index (LAI) combined with a vegetation threshold set, the monitoring area (the management area of ​​the shelterbelt network already identified as severely degraded) is assessed for risk. If both vegetation canopy porosity and LAI are greater than the corresponding vegetation threshold set, the monitoring area is determined to be a low-risk area; otherwise, the absolute difference between vegetation canopy porosity and LAI and the vegetation threshold set is calculated to obtain the results for each vegetation detection. The vegetation detection differences include the difference in canopy porosity and the difference in leaf area index. A vegetation detection difference threshold is obtained, which includes both the canopy porosity difference threshold and the leaf area index difference threshold. If either canopy porosity or leaf area index is below the corresponding vegetation threshold set, and only one of the vegetation detection differences is above the threshold, then the monitored area is classified as a medium-risk area; otherwise, it is classified as a high-risk area. The vegetation threshold set includes the canopy porosity threshold and the leaf area index threshold.

[0028] In this embodiment, when both the vegetation canopy porosity and leaf area index are greater than the corresponding vegetation threshold set, it indicates that the vegetation growth is still within an acceptable range, the water conditions are not significantly abnormal, and the canopy structure has not undergone significant degradation or damage. Although local indicators may fluctuate, the overall ecological function still possesses self-regulating capabilities. Therefore, this area is classified as low-risk, and no adjustments to the stand structure data are made to avoid unnecessary structural changes. This ensures that system intervention only occurs in areas where there is a genuine need, thereby improving the overall stability and sustainability of the shelterbelt network.

[0029] When either canopy porosity or leaf area index falls below the corresponding vegetation threshold set, and only one of the vegetation detection differences exceeds the threshold, the area is classified as a medium-risk zone. This indicates that the forest protection network has deviated from its normal stable range in at least one key ecological dimension. This deviation has objective quantitative evidence and is no longer within the normal fluctuation range, indicating that degradation has occurred. However, in the difference analysis, only one indicator shows a significantly abnormal deviation, meaning that the current degradation is mainly caused by a single dominant factor, and the systemic coupling relationship between vegetation structure, water conditions, and growth status has not yet been fully disrupted. Classifying the area as a medium-risk zone enables accurate early identification of degradation. Before degradation expands into a multi-factor synergistic imbalance, potential problem areas are identified in advance, allowing for only local parameter optimization and avoiding excessive intervention in structural reconstruction.

[0030] When a high-risk area is identified, it indicates that the degradation of the protective forest network has evolved from a single-factor anomaly to a multi-factor synergistic imbalance. The coupling relationship between vegetation structure, water status and growth function has been disrupted. Therefore, identifying a high-risk area can promptly trigger high-precision monitoring and in-depth structural reconstruction, preventing further degradation from spreading and causing the failure of protective functions, thereby improving the safety and reliability of overall restoration.

[0031] By introducing a tiered classification mechanism for medium- and high-risk areas, medium- and high-risk classifications are initiated when either vegetation canopy porosity or leaf area index falls below its corresponding threshold. Furthermore, the uniqueness of vegetation detection differences distinguishes between single-factor anomalies and multi-factor synergistic anomalies, enabling accurate identification in the early stages of degradation. This method avoids excessive structural reconstruction in areas with only a single anomalous factor, while promptly triggering high-intensity monitoring and reconstruction decisions in areas with significant deviations in multiple indicators. This achieves a tiered response and gradual optimization of shelterbelt network degradation restoration, comprehensively improving the targeting, stability, and restoration efficiency of shelterbelt network structural reconstruction.

[0032] Furthermore, the stand structure data and IoT sensor network data are adjusted. Specifically, based on the risk zone assessment results of the area to be monitored, if the area is low-risk, no adjustment to the stand structure data is made; if the area is medium- or high-risk, the stand structure data is adjusted according to the regional restoration goals and vegetation type. The stand structure data includes planting patterns and density. The specific adjustment process involves combining... Figure 5 The diagram shown is a classification map of restoration targets and vegetation types in the region, which is explained in conjunction with the risk types of the monitored area: In this example, the regional restoration target is defined as a medium-risk area based on the ecological function of degraded forests. The corresponding vegetation types are three: windbreak and sand-fixing, ecological and economic, and multi-functional. Therefore, in this area, the planting pattern and density are adjusted to *Salix psammophila* and 1013 (trees / hm²). 2 Similarly, if the regional restoration target is defined as high-risk areas based on vegetation type to identify degraded forests, then the corresponding vegetation types are six: new farmland shelterbelts, coniferous windbreak and sand-fixing forests, coniferous or broad-leaved mixed forests, sparse forest windbreak and sand-fixing forests, tree-shrub mixed windbreak and sand-fixing forests, and shrub sand-fixing forests. Therefore, in two different areas, the planting pattern and density will be adjusted to *Salix psammophila* and 2000 (plants / hm²) respectively. 2 ), sand willow and 3621 (plants / hm) 2 ).

[0033] In this embodiment, the acquisition of forest stand structure data by UAVs mainly relies on image matching and 3D reconstruction. Since the spatial structure of medium-risk or high-risk areas remains continuous and the problems are concentrated in local anomalies, adjusting planting patterns and densities can improve the accuracy of local modeling while avoiding excessive increases in data volume and processing burden.

[0034] Furthermore, a set of vegetation regulation parameters is obtained. Specifically, this involves: acquiring visible light images and 3D point cloud data using a drone equipped with multiple sensors; inputting the 3D point cloud data into a 3D point cloud processing and modeling platform to generate a normalized digital surface model; inputting the visible light images and the normalized digital surface model into a deep learning model to obtain stand structure data; extracting the canopy porosity and leaf area index of the monitored area; acquiring the adjusted IoT sensor network vegetation parameters, including soil moisture content and soil conductivity; processing these parameters to obtain the average soil moisture stress index and average salt stress index of the monitored area; and analyzing the canopy porosity, leaf area index, average soil moisture content, and average soil moisture stress index in the sky-ground collaborative monitoring system to obtain the target canopy vegetation density.

[0035] The specific method of the target vegetation restoration mode is as follows: By combining the target canopy vegetation density in the sky-ground collaborative monitoring system with the regional restoration target of the shelterbelt network, the target restoration mode corresponding to the sky-ground collaborative monitoring system is determined; wherein, the target restoration mode includes at least the target vegetation type, the restoration mode type and the planting density; the target vegetation type includes at least the windbreak and sand-fixing type, the ecological and economic type, the coniferous windbreak type and the mixed tree and shrub type; the restoration mode type includes at least the enclosure mode and the mixed mode.

[0036] Specifically, if the target canopy vegetation density falls within the first density range preset in the database, and the regional restoration target is to identify degraded forests based on ecological function, then the target vegetation type is windbreak and sand-fixing shelterbelt, the restoration pattern type is a mixed poplar and Scots pine pattern, and the planting density is 650-750 (trees / hm²). 2 ).

[0037] By coupling the target canopy vegetation density with the regional restoration target and establishing a correspondence with preset density intervals in the database, the determination of target vegetation type and restoration mode no longer relies on experience-based judgment but is automatically matched based on quantitative indicators. This improves the objectivity and repeatability of shelterbelt network restructuring decisions. The target vegetation restoration mode is generated driven by monitoring data and acts inversely on the restructuring plan, forming a closed-loop control mechanism of monitoring-diagnosis-matching-restructuring-remonitoring, thereby enhancing the dynamic optimization capability of the shelterbelt network in the process of functional improvement.

[0038] The aforementioned set of vegetation regulation parameters, in addition to including target canopy vegetation density and target vegetation restoration patterns, also includes the restoration strategies, principles, and objectives for degraded shelterbelts. Specifically, for example... Figure 3 The diagram illustrates the restoration work of degraded shelterbelts in specific areas. Figure 3 From the forest stand type, restoration goals, patterns, technical indicators, and key technical points, we can see that different forest stand types have different pattern requirements and corresponding technical indicators and key technical points. For example, when the forest stand type is mildly to moderately degraded, its pattern can be divided into four different mixed planting patterns. Different technical indicators and key points can be achieved based on these four different mixed planting patterns. Therefore, through... Figure 3 By adopting the protective forest construction layout method and combining it with the target canopy vegetation density and vegetation restoration mode mentioned above, different protective forest restoration effects can be achieved in different zones in this application.

[0039] In this embodiment, a drone equipped with multiple sensors is used to collect aerial monitoring data of the area to be monitored, including visible light image data and 3D point cloud data. The 3D point cloud data is generated by multi-view image matching or laser scanning, reflecting the 3D spatial coordinate information of the land surface and vegetation. The 3D point cloud data is then input into a 3D point cloud processing and modeling platform for processing. This platform performs denoising, registration, and land cover classification on the raw point cloud data, and constructs a 3D model reflecting the spatial structure characteristics of vegetation and the land surface based on the distinction between ground points and non-ground points. The platform generates a normalized digital surface model, which is a height model obtained by subtracting a digital terrain model from the digital surface model. This model represents the true height information of vegetation relative to the ground and is a three-dimensional spatial structure model that accurately reflects the height, hierarchical structure, and spatial distribution characteristics of the forest canopy. Visible light imagery and the normalized digital surface model are used as joint inputs to a pre-trained deep learning model. The output is the forest stand structure data of the area to be monitored. The deep learning model is a forest stand structure recognition model based on convolutional neural networks. It can automatically identify and extract parameters of forest stand structure by fusing feature information from spectral information and 3D height information of 2D images. By using the deep learning model, the problems of canopy overlap and occlusion misjudgment caused by relying solely on 2D images can be avoided, thus significantly improving the accuracy of forest stand structure data extraction.

[0040] Forest stand structure data includes: canopy height information, canopy coverage, tree spatial distribution density, and canopy gap distribution characteristics.

[0041] The process of obtaining the average soil moisture stress index and average salt stress index of the monitoring area is as follows: the soil moisture data collected by the Internet of Things sensor network in the area to be monitored is statistically averaged over a time scale and normalized and compared with the suitable soil moisture content threshold of the corresponding vegetation to obtain the soil moisture stress index reflecting the degree of water shortage; the collected soil electrical conductivity data is spatially averaged in the region and normalized in combination with the preset salt stress threshold to obtain the salt stress index characterizing the degree of influence of soil salinity on vegetation growth.

[0042] The method for obtaining the target canopy vegetation density is as follows: Obtain the vegetation index system adjustment coefficients (pre-stored, a fixed value used for dimensional matching, e.g., multiply by 1000 for meters to millimeters) corresponding to the canopy porosity and leaf area index, and multiply them to obtain the vegetation index influence value. Obtain the average soil moisture stress index and its corresponding average soil moisture stress index system adjustment coefficient (pre-stored, a fixed value used for dimensional matching, e.g., multiply by 1000 for meters to millimeters), and multiply them to obtain the average soil moisture stress index influence value. Subtract the average soil moisture stress index influence value from the vegetation index influence value to obtain the canopy projection density adjustment coefficient. Multiply the canopy projection density adjustment coefficient by the current canopy projection density to obtain the target canopy vegetation density.

[0043] Furthermore, the division of regions is obtained through the following method: Soil and water distribution parameters from the integrated soil and water distribution map are acquired, including soil moisture content and soil salinity. These parameters are then linearly normalized to obtain a standard set of soil and water distribution parameters, including standard soil moisture content and standard soil salinity. The absolute difference between the two parameters in the standard set is then calculated to obtain the difference values ​​for moisture and salinity and moisture depth. These differences are then averaged to obtain a comprehensive difference degree. Based on a linear mapping of the comprehensive difference degree, the corresponding division length is obtained. The area to be monitored is then divided using these division lengths to obtain the respective division regions.

[0044] In this embodiment, to achieve refined and differentiated spatial decision-making during the reconstruction of the shelterbelt network structure, an integrated soil and water distribution map is constructed based on a sky-ground collaborative monitoring system. This integrated soil and water distribution map is used to divide the monitoring area into regions, resulting in each region. The specific method for constructing the integrated soil and water distribution map based on the sky-ground collaborative monitoring system is as follows: Remote sensing data (vegetation canopy porosity, leaf area index) and measured data from IoT sensors (soil moisture content, soil salinity) are acquired from the sky-ground collaborative monitoring system. The remote sensing data is used to characterize the regional-scale distribution characteristics of soil moisture content, while the measured data from IoT sensors is used to characterize the locational characteristics of soil salinity. After spatial registration and scale unification processing of the soil moisture content and soil salinity data, they are input into a functional platform to generate an integrated soil and water distribution map characterizing the comprehensive distribution characteristics of moisture and salinity conditions in the monitoring area.

[0045] The monitoring area is divided using an integrated soil and water distribution map. The specific method is as follows: The monitoring area is obtained. If the monitoring area is irregular, the two points furthest apart within the area are identified, and the vertical distance between these two points is taken as the maximum length of the area, thus dividing the area. If the monitoring area is regular, the comprehensive difference degree is obtained. Based on a preset comprehensive difference degree-division length mapping rule, the comprehensive difference degree is linearly mapped inversely to obtain the corresponding division length. Based on the obtained division length, the monitoring area is spatially divided to complete the area division, resulting in multiple divided areas.

[0046] This linear mapping method divides areas with drastic changes in site conditions into smaller, more homogeneous regional units. For areas with relatively homogeneous site conditions, a larger division length is used for overall division, allowing the regional division scale to adapt to the complexity of the environment and enhancing the flexibility and universality of the method.

[0047] Based on the integrated water and soil distribution map, the site types of each zone are obtained. The site types include hydrological zoning, land use zoning, and soil zoning. Among them, the hydrological zoning includes soil moisture content level zones; the soil zoning includes alkaline soil zones, salinized zones, and saline soil zones; and the land use zoning includes farmland zones, oasis zones, and transitional zones.

[0048] Furthermore, the structural reconstruction signal of the protective forest network is obtained. The specific method is as follows: the vertical structure of each division area is obtained by matching the hydrological zoning type with the vertical structure configuration set; a preset tree species selection decision table is obtained, and the recommended tree species for each division area is obtained by matching the tree species selection decision table with the hydrological zoning type, land use zoning type, soil zoning type and vertical structure of the site type.

[0049] Recommended tree species for each zone are obtained by matching the tree species selection decision table with the hydrological, land use, and soil zoning types of the site. The specific method is as follows: Obtain the tree species selection decision table (which can be compiled based on the growth habits of tree species, for example, by querying native tree species and their corresponding growth habits from the National Forestry Network). The tree species selection decision table includes: tree species category, drought resistance, salt tolerance, adaptability, and resistance to diseases and pests. Match the site type and vertical structure of each zone with the tree species selection decision table. Tree species that simultaneously meet the requirements of the hydrological, land use, soil zoning, and vertical structure of each zone are considered recommended tree species.

[0050] like Figure 4 As shown, Figure 4This is a schematic diagram of a system for reconstructing and enhancing the function of a degraded shelterbelt network, provided in an embodiment of this application. The system includes: a degradation status analysis module, a risk response acquisition and control module, a vegetation control parameter generation module, and a region division and structural reconstruction module. The degradation status analysis module is used to establish a sky-ground collaborative monitoring system to obtain remote sensing data. Based on the remote sensing data, it performs a degradation diagnosis of the shelterbelt network to obtain its degradation status. When the diagnosed status is severe degradation, it triggers structural reconstruction of the shelterbelt network; otherwise, it maintains the existing status. The risk response acquisition and control module is used to determine the risk zone of the monitored area based on remote sensing data if shelterbelt network structural reconstruction is performed, and obtain the risk zone determination result. The forest stand structure data and IoT sensor network data are adjusted based on the risk zone assessment results. The vegetation regulation parameter generation module is used to acquire the adjusted forest stand structure data and IoT sensor network data, and combine them with remote sensing data as data for the sky-ground collaborative monitoring system. The vegetation regulation parameter set is comprehensively processed to obtain a vegetation regulation parameter set, which includes at least the target canopy vegetation density and the target vegetation restoration mode. The regional division and structural reconstruction module is used to construct an integrated soil and water distribution map based on the sky-ground collaborative monitoring system data, divide the region, obtain each divided region, acquire the site type of each divided region, including hydrological zoning type, land use zoning type and soil zoning type, and obtain the shelterbelt network structure reconstruction signal by combining the vegetation regulation parameter set, and execute the shelterbelt network structure reconstruction scheme.

Claims

1. A method for reconstructing and enhancing the function of degraded shelterbelt networks, characterized in that, Includes the following steps: A sky-ground collaborative monitoring system is established to obtain remote sensing data. Based on the remote sensing data, the degradation status of the shelterbelt network is diagnosed to obtain the degradation status of the shelterbelt network. When the diagnosed status is severe degradation, the shelterbelt network structure is reconstructed; otherwise, the existing status is maintained. If the shelterbelt network structure is reconstructed, the risk zone of the area to be monitored is determined based on remote sensing data, and the risk zone determination results are obtained. The forest stand structure data and IoT sensor network data are then adjusted according to the risk zone determination results. The adjusted forest stand structure data and IoT sensor network data are acquired and combined with remote sensing data to form a sky-ground collaborative monitoring system. The vegetation regulation parameter set is obtained through comprehensive processing. The vegetation regulation parameter set includes at least the target canopy vegetation density and the target vegetation restoration mode. An integrated water and soil distribution map is constructed based on data from the sky-ground collaborative monitoring system. The regions are divided to obtain the site types of each region, including hydrological zoning, land use zoning, and soil zoning. Combined with the vegetation regulation parameter set, the shelterbelt network structure reconstruction signal is obtained, and the shelterbelt network structure reconstruction scheme is executed.

2. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 1, characterized in that, The specific method for obtaining the degradation status of the protective forest network is as follows: Remote sensing data was acquired and processed to obtain vegetation canopy porosity and leaf area index. Based on the vegetation canopy porosity and leaf area index, the degradation index of the protective forest network is calculated and compared with the degradation threshold to determine the degradation status of the protective forest network. When the degradation index of the protective forest network is greater than the degradation threshold, the degradation status of the protective forest network is determined to be severe degradation; otherwise, the degradation status of the protective forest network is determined to be normal.

3. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 2, characterized in that, The analysis methods for vegetation canopy porosity and leaf area index are as follows: After preprocessing the images using remote sensing image processing software, the vegetation index information was calculated using band calculation tools. At the same time, when measuring the leaf area index in the field in the study area, actual sampling points of the vegetation canopy were collected according to the principles of vertical and horizontal distribution of vegetation, and the leaf area index of the vegetation was obtained by measuring the canopy with a plant canopy analyzer. Then, a spatial relationship was established between the ground-measured canopy leaf area index obtained from the sampling and the leaf area index obtained from the remote sensing images. The relationship between the two was fitted to obtain the corresponding inversion model. Finally, the vegetation canopy porosity and leaf area index of the study area were obtained by inversion mapping. Canopy porosity refers to the proportion of pixels in the sky region to the total number of pixels in a certain area of ​​a vegetation canopy hemispherical image. There is a negative correlation between leaf area index and canopy porosity.

4. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 1, characterized in that, The specific method for determining the risk zone is as follows: Based on vegetation canopy porosity and leaf area index, combined with vegetation threshold sets, the risk zone of the area to be monitored is determined. If both vegetation canopy porosity and leaf area index are greater than the corresponding vegetation threshold sets, the risk zone of the area to be monitored is determined to be a low-risk area. Otherwise, the absolute difference between vegetation canopy porosity and leaf area index and vegetation threshold sets is processed to obtain the detection difference of each vegetation. The detection difference of each vegetation includes the difference of vegetation canopy porosity and the difference of leaf area index. Obtain the vegetation detection difference threshold, which includes the vegetation canopy porosity difference threshold and the leaf area index difference threshold; If either the vegetation canopy porosity or the leaf area index is below the corresponding vegetation threshold set, and only one of the vegetation detection differences is above the vegetation detection difference threshold, then the risk zone determination result of the area to be monitored is a medium-risk area; otherwise, the risk zone determination result of the area to be monitored is a high-risk area. The vegetation threshold set includes the vegetation canopy porosity threshold and the leaf area index threshold.

5. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 1, characterized in that, The specific method for adjusting the forest stand structure data and IoT sensor network data is as follows: Based on the risk zone determination results of the area to be monitored, if the area to be monitored is a low-risk area, no adjustment will be made to the forest stand structure data; If the area to be monitored is a medium-risk or high-risk area, the forest stand structure data will be adjusted according to the regional restoration goals and vegetation type. The forest stand structure data includes planting patterns and densities.

6. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 1, characterized in that, The method for obtaining the vegetation regulation parameter set is as follows: Visible light images and 3D point cloud data are acquired by drones equipped with multiple sensors. The 3D point cloud data is then input into a 3D point cloud processing and modeling platform to generate a normalized digital surface model. The visible light images and the normalized digital surface model are then input into a deep learning model to obtain forest stand structure data. The vegetation canopy porosity and leaf area index of the area to be monitored are then extracted. The adjusted vegetation parameters of the IoT sensor network are obtained. The vegetation parameters include soil moisture content and soil electrical conductivity, as well as the average soil moisture content index and average salt stress index of the monitored area. The target canopy vegetation density was obtained by analyzing the vegetation canopy porosity, leaf area index, average soil moisture content, and average soil moisture stress index in the sky-ground collaborative monitoring system.

7. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 6, characterized in that, The specific method of the target vegetation restoration mode is as follows: By combining the target canopy vegetation density in the sky-ground integrated monitoring system with the regional restoration target of the shelterbelt network, the target restoration model corresponding to the sky-ground integrated monitoring system is determined. The target restoration pattern includes at least the target vegetation type, the restoration pattern type, and the planting density; The target vegetation types include at least windbreak and sand-fixing type, ecological and economic type, coniferous windbreak type, and mixed tree and shrub type; The recovery mode types include at least the enclosure mode and the mixed breeding mode.

8. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 1, characterized in that, The specific method for obtaining each divided region is as follows: The soil and water distribution parameters of the integrated soil and water distribution map are obtained. The soil and water distribution parameters include soil moisture content and soil salinity. The soil and water distribution parameters are linearly normalized to obtain a standard set of soil and water distribution parameters, which includes standard soil moisture content and standard soil salinity. Based on the standard set of soil and water distribution parameters, the area to be monitored is divided into regions to obtain each region.

9. The method for reconstructing and enhancing the function of degraded shelterbelt networks as described in claim 1, characterized in that, The specific method for obtaining the protective forest network structure reconstruction signal is as follows: The vertical structure of each region is obtained by matching the hydrological zoning type with the vertical structure configuration set. Obtain a preset tree species selection decision table, and match the tree species selection decision table with the site type, hydrological zoning type, land use zoning type, soil zoning type and vertical structure to obtain recommended tree species for each zoning area.

10. A system applying the method for reconstructing and enhancing the function of a degraded shelterbelt network as described in any one of claims 1-9, characterized in that, include: The module includes a degradation status analysis module, a risk response acquisition and control module, a vegetation control parameter generation module, and a regional division and structural reconstruction module. The degradation status analysis module is used to build a sky-ground collaborative monitoring system, obtain remote sensing data, perform degradation diagnosis of the shelterbelt network based on the remote sensing data, and obtain the degradation status of the shelterbelt network. When the diagnosed status is severe degradation, the shelterbelt network structure is reconstructed; otherwise, the existing status is maintained. The risk response acquisition and control module is used to determine the risk area of ​​the monitored area based on remote sensing data if the shelterbelt network structure reconstruction is performed, obtain the risk area determination result, and adjust the forest stand structure data and IoT sensor network data according to the risk area determination result. The vegetation regulation parameter generation module is used to acquire adjusted forest stand structure data and Internet of Things sensor network data, and combine them with remote sensing data as data for a sky-ground collaborative monitoring system. The vegetation regulation parameter set is obtained through comprehensive processing. The vegetation regulation parameter set includes at least the target canopy vegetation density and the target vegetation restoration mode. The regional division and structural reconstruction module is used to construct an integrated water and soil distribution map based on data from the sky-ground collaborative monitoring system, divide the region, obtain each divided region, acquire the site type of each divided region, including hydrological zoning type, land use zoning type and soil zoning type, and obtain the protective forest network structure reconstruction signal by combining the vegetation regulation parameter set, and execute the protective forest network structure reconstruction scheme.

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