Evaluation method for stability of ecological system in arid region

Abstract

The invention relates to the technical field of ecological protection, and discloses a method for evaluating the stability of an ecological system in an arid region, which comprises the following steps: S1, constructing an index system, S2, performing multi-source remote sensing fusion, S3, performing response modeling on the ecological system, and S4, constructing a stability evaluation matrix in a sensitivity dimension; the method comprises the following steps: S1, constructing four-quadrant stability classification to carry out index weighting, S5, intelligently collecting ground sample points, and then combining an ecological change hot area graph, S6, carrying out model dynamic feedback optimization, and S7, outputting a result and carrying out zoning application, namely dividing a key protection and restoration area and supporting regional ecological safety pattern construction and continuous management. Through fusion of remote sensing data and intelligent acquisition and analysis of ground samples, dynamic updating and real-time monitoring of stability evaluation of the ecological system in the arid region are realized, and the effects of accurately reflecting ecological changes and providing efficient and timely data support for decision makers are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological protection, and specifically to a method for evaluating the stability of arid area ecosystem. Background Art

[0002] Arid area ecosystems are extremely vulnerable and complex, and are greatly affected by climate change, land use change, and human interference. Therefore, evaluating their stability and conducting ecological monitoring have important ecological protection significance. Traditional arid area ecological monitoring usually relies on ground surveys and remote sensing data;

[0003] By regularly sampling and analyzing the ecological status, but these methods have great limitations. The ground survey data has low collection frequency, high cost, and is restricted by ground environmental conditions, while remote sensing data has problems such as mismatched spatio-temporal resolution and lag in data processing, resulting in the ecological assessment results often lacking timeliness and accuracy. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for evaluating the stability of arid area ecosystem, which solves the problem that the existing ecological monitoring methods usually rely on remote sensing data or ground sample data for separate analysis. Although remote sensing data has a wide coverage range, its resolution is low and it cannot effectively reflect the detailed changes of local ecosystems. While ground sample data is accurate, due to limited sampling points, it is difficult to comprehensively reflect the ecological status of the entire region. This monitoring method with a single data source leads to deficiencies in the accuracy and comprehensiveness of the evaluation results.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for evaluating the stability of arid area ecosystem, comprising the following steps:

[0006] S1. Construct an index system: First, according to the characteristics of arid area ecosystems, construct an index system covering the dual dimensions of the importance of ecological environment sensitivity and ecological service functions, screen the indexes through literature data and regional characteristics, and perform standardization processing;

[0007] S2. Multi-source remote sensing fusion: Secondly, use remote sensing data sources, and adopt a spatio-temporal fusion model and a deep learning time series modeling method to construct a dynamic sequence of ecological indexes with high spatio-temporal resolution;

[0008] S3. Ecosystem response modeling: Then, in the sensitivity dimension, based on the formation mechanism of ecological problems, use the USLE model to evaluate the soil erosion risk, and combine with element quantification to simulate the response intensity and spatial distribution of the regional ecosystem to interference;

[0009] S4. Construct a stability evaluation matrix: Then, by overlaying the two-dimensional space of ecological environment sensitivity and the importance of ecological service functions, construct a four-quadrant stability classification to weight the indicators.

[0010] S5. Intelligent collection of ground sample points: Then, combined with the ecological change hot spot map, mobilize the public to participate in crowdsourcing monitoring, and then use AI image recognition technology to label the ecological type and status tags as model training and correction samples.

[0011] S6. Dynamic feedback optimization of the model: Through a three-layer feedback mechanism, update and correct the sensitivity and functionality model parameters in real time to improve the accuracy and robustness of model prediction, and form a sustainable iterative update stability evaluation system.

[0012] S7. Result output and zonal application: Finally, output the regional ecosystem according to the evaluation results, divide the key protection and restoration areas, and support the construction and continuous management of the ecological security pattern in the area.

[0013] Preferably, in S1, the sensitivity indicators include the sensitive levels of ecological environment problems such as soil erosion, desertification, salinization, rocky desertification and urban disturbance. The functionality indicators include the service capabilities of water conservation, biodiversity protection, soil conservation and nutrient retention. The ecosystem characteristics include biological community characteristics, ecological environment characteristics and ecological process characteristics. The standardization process includes data standardization, index normalization and data quality control. The literature materials and regional characteristics include literature materials and regional characteristics.

[0014] Preferably, in S2, the remote sensing data sources include MODIS and Landsat. The spatio-temporal fusion model includes STARFM. The deep learning time series modeling method includes LSTM combined with spatial attention mechanism. The dynamic sequence of ecological indicators includes NDVI, EVI, SAVI. The dynamic sequence of ecological indicators is used to solve the spatio-temporal accuracy conflict problem of remote sensing data and generate a continuous and stable data basis for regional ecosystem changes.

[0015] Preferably, in S3, the elements include wind erosion indicators, groundwater level and frequency of sand-raising winds. The response intensity and spatial distribution are used in the functional dimension, through parameters such as the degree of regional dependence on ecological services, the scope of ecological contribution and the scarcity of ecological resources, and then calculate the service function weights and perform spatial mapping to form a distribution map of the importance of ecological service functions. The formation mechanism of ecological problems includes soil erosion, land desertification and biodiversity reduction.

[0016] Preferably, in S4, the four - quadrant stability classification includes high - sensitivity - high - function, high - sensitivity - low - function, low - sensitivity - high - function, and low - sensitivity - low - function, corresponding to the ecological core protection area, the ecological restoration priority area, the ecological utilization optimization area, and the general monitoring area respectively. The index weighting is carried out by using the analytic hierarchy process or the entropy weight method to generate a regional comprehensive stability index map.

[0017] Preferably, in S5, the ecological change hot - spot map adopts the method of drone cruising + portable sensor layout, automatically scheduling ground sample collection tasks in areas with strong ecological variation. The public participation crowdsourcing monitoring is used to collect ecological status data with time, location, and image information.

[0018] Preferably, in S6, the three - layer feedback mechanism includes constructing remote - sensing data - ground sample - AI recognition. The three - layer feedback mechanism is used for the conversion from static evaluation to dynamic monitoring and early warning. The correction sensitivity includes the correction of soil erosion sensitivity, the sensitivity correction for water resources in arid areas and spatial - difference sensitivity. The functional model parameters include ecological service function value parameters and ecological service function spatial - distribution parameters.

[0019] Preferably, in S7, the key protection and restoration areas include the stability - level map, the sensitive - function composite zoning map, and the list of key protection and restoration zoning, which provide a scientific basis for land - use planning in arid areas, the identification of ecological protection priority areas, policy resource allocation, etc.

[0020] The present invention provides a method for evaluating the stability of an arid - area ecosystem, having the following beneficial effects:

[0021] 1. By integrating the intelligent collection and analysis of remote - sensing data and ground samples, the present invention realizes the dynamic update and real - time monitoring of the stability assessment of the arid - area ecosystem, obtaining the effect of accurately reflecting ecological changes and providing efficient and timely data support for decision - makers.

[0022] 2. By constructing a two - dimensional matrix based on sensitivity and service function, the present invention realizes the scientific and precise zoning of the arid - area ecosystem, obtaining the effect of providing clear guidance for regional ecological protection, restoration, and utilization, and supporting the optimization of resource allocation and management.

[0023] 3. By constructing a three - layer feedback mechanism of remote - sensing data, ground samples, and AI recognition, the present invention realizes the seamless conversion from static ecological assessment to dynamic monitoring and early warning, obtaining the effect of enhancing the prediction ability of ecosystem stability and timely identifying potential ecological risks.

[0024] 4. Through the comprehensive use of drone cruising, portable sensor deployment, and public crowdsourcing monitoring, the present invention realizes an ecological monitoring system with low cost and wide coverage, obtains real-time ecological status data, and improves the coverage and public participation of ecological protection work. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of a method for evaluating the stability of an ecological system in an arid area according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to the attached Figure 1 , an embodiment of the present invention provides a method for evaluating the stability of an ecological system in an arid area, including the following steps:

[0028] S1. Construct an index system: First, according to the characteristics of the ecological system in the arid area, construct an index system covering the dual dimensions of the sensitivity of the ecological environment and the importance of ecological service functions, screen the indexes through literature data and regional characteristics, and perform standardization processing;

[0029] S2. Multi-source remote sensing fusion: Secondly, use remote sensing data sources, and adopt a spatio-temporal fusion model and a deep learning time series modeling method to construct a dynamic sequence of ecological indexes with high spatio-temporal resolution;

[0030] S3. Ecological system response modeling: Then, in the sensitivity dimension, based on the formation mechanism of ecological problems, use the USLE model to evaluate the soil erosion risk, and combine the elements to quantitatively simulate the response intensity and spatial distribution of the regional ecological system to disturbances;

[0031] S4. Construct a stability evaluation matrix: Then, by superimposing the two-dimensional space of ecological environment sensitivity and ecological service function importance, construct a four-quadrant stability classification for index weighting;

[0032] S5. Intelligent ground sample collection: Then, in combination with the ecological change hot spot map, at the same time mobilize the public to participate in crowdsourcing monitoring, and then use AI image recognition technology to label the ecological types and status labels as model training and correction samples;

[0033] S6. Model dynamic feedback optimization: Through a three-layer feedback mechanism, the sensitivity and functional model parameters are updated and corrected in real time to improve the accuracy and robustness of the model prediction, and a sustainable iterative update stability evaluation system is formed;

[0034] S7, Result Output and Zoning Application: Finally, based on the evaluation results, the regional ecosystem is output, and key protection and restoration areas are demarcated, and the ecological security pattern construction and sustainable management of the support area are supported.

[0035] In S1, the sensitivity indicators include the sensitive levels of soil erosion, desertification, salinization, rocky desertification, and urban disturbance of the ecological environment. The functional indicators include the service capabilities of water conservation, biodiversity protection, soil conservation, and nutrient retention. The ecosystem characteristics include biological community characteristics, ecological environment characteristics, and ecological process characteristics. The standardization process includes data standardization, index normalization, and data quality control. The literature materials and regional characteristics include literature materials and regional characteristics.

[0036] Specifically, first, by constructing a sensitivity indicator system, the response of the arid ecosystem to external disturbances is evaluated. The sensitivity of soil erosion is calculated by the USLE model to obtain the soil loss amount and evaluated in combination with factors such as climate, topography, and vegetation. The sensitivity of desertification is evaluated by combining the humidity index and the sand-driving wind frequency with the soil dry-wet cycle data. The sensitivity of salinization is determined by the groundwater level change, salinity, and evaporation data. The sensitivity of rocky desertification is analyzed by combining geological, climatic, and hydrological conditions with the adaptability of the biological community. The sensitivity of urban disturbance is evaluated by the population density, the proportion of construction land, and the traffic flow data.

[0037] Secondly, by constructing a functional indicator system, the service capabilities of the ecosystem in water conservation, biodiversity protection, soil conservation, and nutrient retention are evaluated. The water conservation function is analyzed by the hydrological cycle, the difference between precipitation and evaporation, and the topographic features. The biodiversity protection function is evaluated by species distribution, diversity index, and habitat connectivity. The soil conservation function is analyzed by the physical and chemical properties of the soil, the vegetation coverage, and the change of land use type. The nutrient retention function is evaluated by the flow and accumulation of nutrients such as nitrogen and phosphorus.

[0038] Then, by analyzing the biological community characteristics, ecological environment characteristics, and ecological process characteristics, the stability of the ecosystem is comprehensively evaluated. The biological community characteristics analyze the biodiversity of the ecosystem through species composition, community structure, and niche distribution. The ecological environment characteristics evaluate the adaptation conditions of the ecosystem by combining natural environmental factors such as climate, geology, topography, and hydrology. The ecological process characteristics analyze the self-maintenance ability of the ecosystem through energy flow and material cycle.

[0039] Finally, through standardization processing, the data is standardized, the indicators are normalized, and data quality control is carried out. Data standardization makes data from different sources comparable; indicator normalization unifies indicators with different units and dimensions into a standard range, facilitating weighted calculation and comprehensive evaluation; data quality control ensures the reliability and accuracy of the data, removing outliers and filling in missing values. According to literature and regional characteristics, applicable model parameters and indicators are determined, providing theoretical support and data basis to offer scientific guidance for the formulation of the ecological assessment model.

[0040] In S2, remote sensing data sources include MODIS and Landsat, the spatio-temporal fusion model includes STARFM, the deep learning time series modeling method includes LSTM combined with spatial attention mechanism, and the dynamic sequence of ecological indicators includes NDVI, EVI, and SAVI. The dynamic sequence of ecological indicators is used to solve the spatio-temporal accuracy conflict problem of remote sensing data and generate a continuous and stable data basis for regional ecosystem changes.

[0041] Specifically, first, through remote sensing data sources, including MODIS and Landsat, ecological system monitoring data in arid regions are collected. These remote sensing data provide ecological information at different time and space scales. MODIS provides medium-resolution data with a higher frequency, while Landsat provides data with a higher resolution. Then, the spatio-temporal fusion model STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) is used to fuse MODIS and Landsat data, thereby overcoming the spatio-temporal resolution mismatch problem of different data sources. The STARFM model can jointly optimize the space and time of images according to spatio-temporal continuity, improve spatio-temporal resolution, and achieve high-quality remote sensing data reconstruction;

[0042] Subsequently, the deep learning time series modeling method, including LSTM (Long Short-Term Memory Network) combined with spatial attention mechanism, is used to further improve the accuracy and robustness of remote sensing data analysis. LSTM can handle the time-dependent relationships in long time series data, while the spatial attention mechanism can strengthen the attention to key regions in remote sensing images and improve the spatial accuracy of regional ecological changes. This combined method can effectively identify and predict the dynamic changes of the ecological system in arid regions, especially in response to complex ecological processes and environmental factors;

[0043] Predict the future trend of the time series of ecological indicators (such as NDVI) generated by STARFM to capture the characteristics of regional dynamic ecological changes.

[0044] LSTM time series update formula:

[0045] f t =σ(Wf ·[h t-1 ,x t +b f )

[0046] i t =σ(W i ·[h t-1 ,x t +b i )

[0047]

[0048] o t =σ(W o ·[h t-1 ,x t +b o )

[0049] h t =o t ·tanh(C t )

[0050] Variable description:

[0051] x t : The value at the t-th moment of the input ecological index (such as NDVI, EVI) sequence · h t : Hidden state (memory), representing the comprehensive state information of the current time node

[0052] C t : Cell state (memory in () state)

[0053] σ, tanh: Activation functions (sigmoid, hyperbolic tangent)

[0054] W * ,b * : Weights and biases of each gating mechanism

[0055] Spatial attention weighting formula (fusing spatial key regions):

[0056]

[0057] Where e i =v T ·tanh(W s ·s i +b s ),-α i : Attention weight of the i-th spatial unit - s i : Feature vector of the i-th spatial unit (such as NDVI sequence) - W s ,v,b s : Learned attention parameters - ∑αi = 1, ensuring spatial weighted consistency

[0058] Spatial-temporal fusion output:

[0059]

[0060] -y t : Predicted value of the fused spatio-temporal ecological index;

[0061] Next, by generating dynamic sequences of ecological indices, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), these indices can accurately reflect the vegetation coverage, soil moisture, and ecological health status of the ecosystem. The generated dynamic sequences of ecological indices provide continuous and stable data support for regional ecosystem changes, effectively solving the problem of spatio-temporal accuracy conflict of remote sensing data, thus providing a high-quality spatio-temporal consistency data basis for ecological monitoring and assessment.

[0062] In S3, the elements include wind erosion index, groundwater level, and frequency of sand-driving winds. The response intensity and spatial distribution are used in the functional dimension through parameters such as the degree of regional dependence on ecological services, the scope of ecological contribution, and the scarcity of ecological resources. Then, by calculating the service function weights and performing spatial mapping, a distribution map of the importance of ecological service functions is formed. The formation mechanisms of ecological problems include soil erosion, land desertification, and biodiversity reduction.

[0063] Specifically, first, by evaluating the wind erosion index, groundwater level, and frequency of sand-driving winds, combined with the geographical and climatic characteristics of the arid region, the ecological sensitivity of the region is analyzed. The wind erosion index can evaluate the susceptibility of the region to wind erosion through factors such as wind speed, wind direction, and vegetation coverage; groundwater level data reflects the changes in regional water sources and has a direct impact on soil moisture and plant growth; the analysis of the frequency of sand-driving winds is used to evaluate the occurrence frequency of sandstorms and their potential threats to the ecosystem. Through these factors, the ecological environment changes in the arid region and the response intensity and spatial distribution of the ecosystem can be evaluated more comprehensively;

[0064] Next, in the functional dimension, based on parameters such as the degree of regional dependence on ecological services, the scope of ecological contribution, and the scarcity of ecological resources, the service functions provided by the arid region ecosystem are evaluated. The degree of regional dependence on ecological services can be measured by the demand for services such as water conservation, soil conservation, and biodiversity. The scope of ecological contribution involves the radiation effect of the ecological functions of the region, evaluating its ecological benefits to the surrounding environment and communities. The scarcity of ecological resources reflects the uniqueness and scarcity of the ecological service functions, especially the pressure on the ecosystem under the background of resource shortage and climate change;

[0065] Subsequently, by calculating the weights of various service functions and combining with spatial mapping technology, the spatial distribution of service functions is superimposed with ecological sensitivity data to form a map of the importance of ecological service functions. This map can clearly show the strengths and weaknesses of ecological service functions in different regions and their importance in the whole region, providing decision-making support for regional ecological protection, restoration and resource management;

[0066] Finally, combined with the formation mechanisms of ecological problems, including soil erosion, land desertification and biodiversity reduction, the assessment of ecosystem stability is further improved. Soil erosion is mainly caused by factors such as climate change, vegetation destruction and unreasonable land use. Land desertification is due to the reduction of precipitation and human activities such as overgrazing, which have exacerbated land degradation. The reduction of biodiversity directly affects the functional stability and self-repair ability of the ecosystem. By analyzing the formation mechanisms of these ecological problems, the ecological risks in arid areas can be more accurately identified and effective ecological restoration countermeasures can be proposed.

[0067] In S4, the four-quadrant stability classification includes high-sensitivity - high-function, high-sensitivity - low-function, low-sensitivity - high-function, and low-sensitivity - low-function, which correspond to the ecological core protection area, the ecological restoration priority area, the ecological utilization optimization area, and the general monitoring area respectively. The analytic hierarchy process or the entropy weight method is used to weight the indicators to generate a regional comprehensive stability index map.

[0068] Specifically, first, through the four-quadrant stability classification method, the region is classified according to the sensitivity and functionality of the ecosystem, resulting in four categories: high-sensitivity - high-function, high-sensitivity - low-function, low-sensitivity - high-function, and low-sensitivity - low-function. High-sensitivity - high-function regions are usually areas with strong ecosystem stability and high sensitivity to external disturbances. These regions usually have high ecological protection value and are thus designated as ecological core protection areas. High-sensitivity - low-function regions refer to areas where the ecosystem shows strong sensitivity when under external pressure, but their ecological service functions are weak. They are usually classified as ecological restoration priority areas and require key restoration and protection. Low-sensitivity - high-function regions show a low response to external disturbances but have strong ecological service functions. These regions can be moderately utilized on the premise of reasonable development and are designated as ecological utilization optimization areas. Low-sensitivity - low-function regions have a small response of the ecosystem to external disturbances and relatively weak ecological service functions. These regions usually do not require much intervention and can be designated as general monitoring areas for long-term tracking and monitoring of the ecological status;

[0069] Next, the Analytic Hierarchy Process (AHP) or the entropy weight method is used to weight each indicator. The AHP constructs a judgment matrix, and experts evaluate the importance weights of each indicator and perform weighting according to their relative importance, so as to obtain the influence degree of each indicator on the final evaluation result. The entropy weight method determines the weights of the indicators automatically by calculating the data dispersion of each indicator. The indicators with larger weights represent the importance of the ecological function or sensitivity in the comprehensive evaluation. Both methods can be used to assign reasonable weights to different indicators to ensure the scientificity and accuracy of the evaluation results;

[0070] Finally, combining the weighted indicators, a regional comprehensive stability index map is generated. This map comprehensively considers the sensitivity and functionality of the ecosystem within the region, intuitively shows the stability levels of each region, and provides a scientific basis and decision-making support for regional ecological management, the division of protection priority areas, ecological restoration, and the utilization of ecological resources.

[0071] In S5, for the ecological change hotspot map, the method of drone cruising + portable sensor layout is adopted. In areas with strong ecological variation, the ground sample collection tasks are automatically scheduled, and public participation in crowdsourcing monitoring is used to collect ecological status data with time, location, and image information.

[0072] Specifically, first, by generating an ecological change hotspot map, the method of combining drone cruising and portable sensor layout is adopted to conduct automatic inspections in areas with strong ecological variation. Drones can cover a large area and provide high-resolution image data for identifying and monitoring changes in the ecological environment, especially in arid areas where drastic changes are likely to occur. Portable sensors can move with the drones and collect ecological monitoring data such as climate, soil moisture, temperature, and wind speed. These data help to accurately identify the change hotspots in the ecosystem and provide data support and decision-making basis for the scheduling of ground sample collection tasks;

[0073] Next, after identifying the areas with strong ecological variation, the system will automatically schedule the ground sample collection tasks. These sampling points are distributed within the hotspots. Through automated task allocation, it is ensured that key ecological change areas can be covered to monitor the ecological status in real time and accurately. The data collected at the ground sample points include environmental factors, plant coverage, soil quality, etc., which can provide important verification support for remote sensing data and model evaluation;

[0074] In addition, to further improve the breadth and accuracy of monitoring, the present invention also introduces a crowdsourcing monitoring mechanism involving public participation. The public participates in ecological monitoring tasks through mobile devices or mini-programs to collect data on the current ecological situation. These data include ecological status pictures and videos taken with time, location, and image information, which are provided for subsequent analysis by experts and the system. Public participation can not only increase the frequency and coverage of data collection but also provide ecological information at a relatively low cost, enhancing the sense of social participation and ecological protection awareness. Through this combined approach of drone flight, portable sensor deployment, ground sampling, and public participation in crowdsourcing monitoring, the changes in the ecosystem can be monitored in real time and comprehensively, providing high-quality data support for further ecological assessment and decision-making.

[0075] In step S6, the three-layer feedback mechanism includes constructing remote sensing data - ground sample points - AI recognition. The three-layer feedback mechanism is used for the conversion from static evaluation to dynamic monitoring and early warning. The correction of sensitivity includes the correction of soil erosion sensitivity, the sensitivity correction for water resources in arid areas and spatial difference sensitivity. The functional model parameters include ecological service function value parameters and ecological service function spatial distribution parameters.

[0076] Specifically, first, a three-layer feedback mechanism of remote sensing data, ground sample points, and AI recognition is constructed to achieve the conversion from static evaluation to dynamic monitoring and early warning. Remote sensing data provides the long-term ecological change trend over a large area, while the data collected from ground sample points provides high-precision and real-time ecological status information. AI recognition technology, especially image recognition and deep learning algorithms, can intelligently process and analyze these remote sensing images and ground sample point data to identify the dynamic characteristics of ecological changes in real time. Through the three-layer feedback mechanism, the remote sensing data and ground sample information continuously complement each other, gradually optimizing the model prediction and improving the accuracy and real-time performance of ecosystem assessment.

[0077] Secondly, the three-layer feedback mechanism corrects the sensitivity parameters in the ecological model, especially the correction of soil erosion sensitivity, water resource sensitivity in arid areas, and spatial difference sensitivity. The correction of soil erosion sensitivity adjusts and corrects the recognition accuracy of erosion areas in a timely manner by comparing remote sensing images and field samples to ensure that the model can accurately predict future soil loss. For the water resource sensitivity in arid areas, the prediction and management strategies for water sources in arid areas are optimized by correcting the water resource consumption and distribution parameters in the model. In addition, the correction of spatial difference sensitivity adjusts the spatial change prediction in the model by analyzing the differences in climate, terrain, vegetation, etc. in different regions, making the evaluation results more in line with the actual geographical and environmental differences.

[0078] Finally, the calibration of functional model parameters involves the optimization of value parameters and spatial distribution parameters of ecological service functions. The value parameters of ecological service functions mainly evaluate the economic and environmental values of various ecological services (such as water conservation, soil conservation, biodiversity protection, etc.) and are updated in combination with spatio-temporal changes. The spatial distribution parameters adjust the spatial distribution prediction of functional services by analyzing the ecological function intensity and spatial patterns in different regions. The continuous optimization of these parameters can help improve the accuracy of regional ecological function assessment. Especially when dealing with the dynamic adjustment of climate change and land use change, it provides a scientific basis for ecological protection and resource management. Through the application of this three-layer feedback mechanism, the static ecosystem evaluation method can be transformed into a dynamic monitoring and early warning system, realizing the real-time tracking and management of ecological risks and providing continuously updated data support for decision-making.

[0079] In S7, the key protection and restoration areas include the stability level map, the sensitive function composite zoning map, and the list of key protection and restoration area divisions, which are used to provide a scientific basis for land use planning in arid areas, the identification of priority ecological protection areas, and the allocation of policy resources.

[0080] Specifically, first, by generating the stability level map, the regional ecosystem is classified according to the evaluation results of its sensitivity and functionality, clearly showing the ecological stability levels of different regions. This map can not only display the ecological system stability of each region in the current state but also predict possible future changes, thus providing effective decision-making support for land use planning in arid areas. Through this stability level map, the vulnerable areas of the ecological system and their functional strengths and weaknesses can be effectively identified, providing data support for relevant departments when formulating land use policies;

[0081] Next, based on the stability level map, the sensitive function composite zoning map is generated. This map comprehensively considers the sensitivity of the ecological system and the distribution of ecological service functions, revealing the comprehensive service value of different functional areas and their contributions to ecological health. By combining the spatial distribution of ecological services with sensitive areas, different types of ecological zones are formed for the refined management of the ecological environment. The sensitive function composite zoning map can not only help decision-makers understand the spatial patterns of different ecological services in the region but also provide a scientific basis for ecological protection and restoration work, clarifying which areas should be given priority protection and which areas can be moderately developed;

[0082] Finally, by generating a list of key protection and restoration zoning, combined with the aforementioned stability level map and sensitive function composite zoning map, areas that need to be key protected and restored in arid regions are systematically identified. These areas usually have relatively high ecological risks or provide important ecological service functions. After being demarcated, they can provide clear guidance for the identification of ecological protection priority areas, the planning of ecological restoration actions, and the scientific allocation of resources. The formulation of the list of key protection and restoration zoning not only provides a systematic and structured working framework for the ecological protection of arid regions, but also provides a scientific basis for the government in aspects such as policy formulation, resource allocation, and supervision and implementation;

[0083] Through the application of these charts and lists, the present invention provides solid theoretical support and practical guidance for land use planning in arid regions, the identification of ecological protection priority areas, and the rational allocation of policy resources, contributing to ecosystem protection and regional sustainable development.

[0084] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An evaluation method for the stability of an arid ecosystem, characterized in that, It includes the following steps: S1. Construct an index system: First, according to the characteristics of the arid ecosystem, construct an index system covering the dual dimensions of the importance of ecological environment sensitivity and ecological service functions. Screen the indexes through literature materials and regional characteristics, and conduct standardization processing; S2. Multi-source remote sensing fusion: Secondly, use remote sensing data sources, and adopt a spatio-temporal fusion model and a deep learning time series modeling method to construct a dynamic sequence of ecological indexes with high spatio-temporal resolution; S3. Ecosystem response modeling: Then, in the sensitivity dimension, based on the formation mechanism of ecological problems, use the USLE model to evaluate the soil erosion risk, and combine elements to quantitatively simulate the response intensity and spatial distribution of the regional ecosystem to disturbances; S4. Construct a stability evaluation matrix: Then, by superimposing the two-dimensional space of ecological environment sensitivity and ecological service function importance, construct a four-quadrant stability classification for index weighting; S5. Intelligent collection of ground sample points: Then, combined with the ecological change hot spot map, mobilize public participation in crowdsourcing monitoring at the same time, and then use AI image recognition technology to label ecological types and status labels as model training and correction samples; S6. Model dynamic feedback optimization: Through a three-layer feedback mechanism, update and correct the sensitivity and functional model parameters in real time, improve the prediction accuracy and robustness of the model, and form a sustainable iterative update stability evaluation system; S7. Result output and zoning application: Finally, output the regional ecosystem according to the evaluation results, divide the key protection and restoration areas, and support the construction and continuous management of the regional ecological security pattern.

2. The method for evaluating the stability of an arid ecosystem according to claim 1, wherein: In S1, the sensitivity indexes include the sensitive levels of ecological environment problems such as soil erosion, desertification, salinization, rocky desertification and urban disturbance. The functional indexes include water conservation, biodiversity protection, soil conservation, nutrient retention and ecological service capacity. The ecosystem characteristics include biological community characteristics, ecological environment characteristics and ecological process characteristics. The standardization processing includes data standardization, index normalization and data quality control. The literature materials and regional characteristics include literature materials and regional characteristics.

3. The method for evaluating the stability of an arid ecosystem according to claim 1, wherein: In S2, the remote sensing data sources include MODIS and Landsat. The spatio-temporal fusion model includes STARFM. The deep learning time series modeling method includes LSTM combined with a spatial attention mechanism. The dynamic sequence of ecological indexes includes NDVI, EVI, SAVI. The dynamic sequence of ecological indexes is used to solve the spatio-temporal accuracy conflict problem of remote sensing data and generate a continuous and stable data basis for regional ecosystem changes.

4. The stability evaluation method of an arid ecosystem according to claim 1, wherein: In S3, the elements include wind erosion indexes, groundwater levels and the frequency of sand-driving winds. The response intensity and spatial distribution are used to, in the functional dimension, through parameters such as the degree of dependence of the region on ecological services, the scope of ecological contributions and the scarcity of ecological resources, and then calculate the service function weights and conduct spatial mapping to form a distribution map of the importance of ecological service functions. The formation mechanism of ecological problems includes soil erosion, land desertification and biodiversity reduction.

5. The stability evaluation method of an arid ecosystem according to claim 1, characterized in that: In S4, the four - quadrant stability classification includes high - sensitivity - high - function, high - sensitivity - low - function, low - sensitivity - high - function, and low - sensitivity - low - function, corresponding to the ecological core protection area, the ecological restoration priority area, the ecological utilization optimization area, and the general monitoring area respectively. The index weighting is carried out by using the analytic hierarchy process or the entropy weight method to generate the regional comprehensive stability index map.

6. The stability evaluation method of an arid ecosystem according to claim 1, wherein: In S5, the ecological change hot - spot map adopts the method of drone cruising + portable sensor layout, automatically scheduling the ground sample collection task in the area with strong ecological variation. The public participation crowdsourcing monitoring is used to collect the ecological status data with time, location, and image information.

7. The method for evaluating the stability of an arid ecosystem according to claim 1, characterized in that: In S6, the three - layer feedback mechanism includes constructing remote - sensing data - ground sample - AI recognition. The three - layer feedback mechanism is used for the conversion from static evaluation to dynamic monitoring and early warning. The correction sensitivity includes the correction of soil erosion sensitivity, the correction of water resource sensitivity in arid areas and spatial - difference sensitivity. The functional model parameters include the ecological service function value parameters and the ecological service function spatial distribution parameters.

8. The method for evaluating the stability of an arid ecosystem according to claim 1, characterized in that: In S7, the key protection and restoration areas include the stability - level map, the sensitive - function composite zoning map, and the list of key protection and restoration area divisions, which provide a scientific basis for land - use planning in arid areas, the identification of ecological protection priority areas, policy resource allocation, etc.

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