Ecological protection red line division method applied to ecological basic investigation

Abstract

The invention relates to the technical field of ecological environment protection, in particular to an ecological protection red line division method applied to ecological basic investigation, which comprises the following steps: evaluating the service function and stability of an ecological unit through ecological basic data collection and processing in combination with an ecological model, and further delimiting an ecological protection red line area. The boundary of the red line area is optimized through space overlay analysis and conflict analysis of the current land utilization situation, and real-time adjustment is carried out based on ecological risk assessment and a dynamic monitoring mechanism. In combination with deep learning and reinforcement learning algorithms, dynamic monitoring and flexible adjustment of the red line area are realized, continuity and safety of an ecological system are guaranteed, intelligent management of the ecological protection red line is realized through a GIS technology and an early warning mechanism, scientificity and effectiveness of the ecological protection red line area are ensured, and the ecological protection red line area management method and system are suitable for popularization and application. And a reliable guarantee is provided for long-term stable development of an ecological system.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment protection, and in particular to an ecological protection red line demarcation method applied to ecological basic survey. Background Art

[0002] With the continuous enhancement of awareness of ecological environmental protection, the demarcation of ecological protection red lines has become an important measure for ecological environmental protection. The ecological protection red line refers to a regional protection range determined based on scientific assessments of ecological functions, ecological services and ecological sensitivity.

[0003] However, the delineation of ecological protection red lines not only involves the collection and processing of large-scale ecological data, but also requires comprehensive consideration of the natural characteristics and socio-economic factors of the region. Traditional red line delineation methods often rely on static ecological function assessments and lack the ability to monitor and dynamically adjust changes in regional ecological risks in real time. In existing technologies, most use fixed red line boundaries and lack a mechanism for real-time updates and responses to sudden ecological risks, resulting in the inability to adjust the red line range in a timely manner when faced with natural disasters, climate change, and human activity interference, affecting the flexibility and accuracy of ecological protection. In addition, due to the relatively single ecological function assessment method, it is impossible to comprehensively consider the stability of the regional ecosystem, the intensity of ecological services, and the ecological sensitivity, resulting in blind spots or over-protection in the process of delineating ecological protection red lines, making it difficult to achieve a balance between ecological protection and socio-economic development. Summary of the invention

[0004] Based on the above purpose, the present invention provides an ecological protection red line demarcation method applied to ecological basic survey.

[0005] A method for demarcating an ecological protection red line applied to an ecological basic survey comprises the following steps: S1, Collection of ecological basic data: Collect ecological basic data in the target area, including vegetation coverage, soil erosion intensity, surface water distribution, land use status data, climate data, human activity distribution data, historical disaster records and remote sensing image data, and perform spatial matching and standardization processing through geographic information system; S2, Ecological function assessment and classification: Based on the collected ecological basic data, ecological models are used to assess the stability of ecological units, the intensity of ecological service functions (water conservation, carbon sink, soil conservation) and ecological sensitivity, and to classify ecological service function levels; S3, Regional ecological function division and redline framework design: Based on the ecological service function level, combined with the current land use status and the boundaries of nature reserves, the ecological protection redline area is delineated, and the protection objectives and development restriction types are clarified; S4, Ecological risk assessment and early warning: Based on the assessment results of S2, an ecological risk model is constructed to assess the risks of natural disasters, climate change and human interference factors in the red line area, and a dynamic early warning mechanism is designed; S5, dynamic monitoring and adjustment mechanism: using the real-time ecological basic data of S1, combining the ecological service function level and risk warning level through machine learning algorithms, dynamically adjust the red line range to respond to the risk warning results of S4.

[0006] Optionally, the S1 includes: S11, multi-source data acquisition and classification: obtain multi-temporal and multi-spectral remote sensing image data of the target area through satellite remote sensing platforms, collect vegetation coverage, soil erosion intensity, surface water distribution and climate data (including precipitation, temperature, wind speed) through ground monitoring stations, obtain land use status data (including the spatial distribution and area proportion of cultivated land, forest land, grassland, construction land, and unused land) through satellite remote sensing interpretation and land survey databases, obtain human activity distribution data (including the spatial distribution of residential areas, industrial land, and transportation networks) through social and economic statistics databases and field surveys, and obtain historical disaster records (including the time, spatial distribution and impact range of floods, landslides, mud-rock flows and other events) through local disaster management department databases and historical remote sensing image inversion; S12, data preprocessing and format standardization: perform radiation correction, atmospheric correction and geometric precision correction on remote sensing image data to generate standard false color synthetic images, perform missing value filling, outlier removal and time series smoothing on ground monitoring data and human activity data, and uniformly convert them into vector or raster formats (such as Shapefile, GeoTIFF) compatible with geographic information systems (GIS); S13, spatial reference system unification: all data are uniformly projected into the same geographic coordinate system (such as WGS84 coordinate system), and a uniform spatial resolution is set, and data scale differences are eliminated through resampling, spatial interpolation and vector-to-raster conversion; S14, spatial matching and attribute association of ecological data: superimpose vegetation coverage, soil erosion intensity, surface water distribution, climate data and human activity distribution data on the GIS platform, perform layer registration based on spatial location, establish an attribute association table, and achieve one-to-one mapping between ecological parameters and geographic units (e.g., each grid unit is associated with vegetation type, erosion level, water source distance, climate factors and human activity intensity); S15, data quality verification and integrated storage: ensure data accuracy through cross-validation (such as comparison of remote sensing data with ground-measured data) and spatial consistency verification (such as smooth transition of data in adjacent areas), and finally integrate the standardized multi-source ecological data into the spatiotemporal database, and store them by timestamp and spatial block classification to support the dynamic call of subsequent ecological function assessment models.

[0007] Optionally, S2 includes: S21, quantification of ecological service function indicators: Based on the ecological basic data collected in S1, the water conservation module in the InVEST model is used to calculate the water conservation capacity, the net primary productivity carbon sink module of the CASA model is used to evaluate the carbon sink intensity, and the soil erosion modulus calculation module of the RUSLE equation is used to evaluate the soil conservation intensity; S22, normalization of functional intensity and weight allocation: normalize the calculated water conservation, carbon sink intensity and soil conservation intensity. Specifically, a linear normalization formula is used to map the minimum value of each service function indicator to 0 and the maximum value to 1. Then, the weight of each function is determined by using the hierarchical analysis method combined with expert scoring, where the weight of water conservation is set to 0.4, the weight of carbon sink is 0.3, and the weight of soil conservation is 0.3. Finally, the normalized functional intensity values ​​are weighted and summed to obtain the comprehensive ecological service function intensity value of each ecological unit. S23, service function level division: Based on the comprehensive ecological service function intensity value, the target area is divided into three levels: high, medium and low using the natural breakpoint method.

[0008] S24, screening and quantification of ecological sensitivity factors: Based on the ecological basic data collected in S1, terrain slope, vegetation coverage, soil erosion intensity, surface water proximity and human activity interference intensity were selected as ecological sensitivity factors, and the quantitative values ​​of ecological sensitivity factors were calculated through GIS spatial analysis methods.

[0009] S25, sensitivity index integration and classification: define the weights of each sensitivity factor, among which the slope weight is 0.25, the vegetation coverage weight is 0.30, the soil erosion intensity weight is 0.20, the surface water proximity weight is 0.15, and the human activity interference intensity weight is 0.10. By weighted superposition of the quantitative values ​​of each factor, an ecological sensitivity index layer is generated, and the sensitivity index is divided into three levels: high, medium, and low using the equal spacing method.

[0010] S26, ecosystem stability assessment: Based on the ecological service function intensity level obtained in S23 and the ecological sensitivity level obtained in S25, an ecosystem stability matrix is ​​constructed, and finally the ecosystem stability assessment results are output.

[0011] Optionally, the S3 includes: S31, Identification of ecological priority protection areas: Based on the divided ecological service function levels (high, medium, and low) and ecological sensitivity levels (high, medium, and low), the "high function-low sensitivity" and "high function-medium sensitivity" areas are screened as priority protection areas through GIS spatial overlay analysis methods, and combined with the boundaries of existing nature reserves, preliminary ecological protection red line candidate areas are delineated; S32, land use conflict analysis and boundary optimization: spatially overlay the land use status data collected in S1 with the red line candidate areas to identify areas where human activity conflicts exist, adjust the conflicting areas, and optimize the boundaries of the affected areas based on the principle of ecological function priority.

[0012] S33, fusion and expansion of protected area boundaries: spatially integrate the existing nature reserve boundaries with the optimized red line candidate areas, and analyze whether there are areas with high ecological service functions outside the nature reserve that have not been included; S34, Protection objectives and development restriction classification: Three levels of management and control rules are defined based on the combination of ecological service function level and ecological sensitivity level within the red line area.

[0013] S35, red line area mapping and attribute association: Through GIS mapping technology, the final ecological protection red line area and its corresponding management and control rules are encoded into a vector layer and associated with detailed attribute table fields.

[0014] Optionally, the S4 includes: S41, risk factor screening and quantification: Based on the divided ecological service function level and the generated ecological sensitivity level, combined with the climate data, human activity distribution data and historical disaster records collected in S1, natural disaster factors (including flood frequency, landslide susceptibility index), climate change factors (including annual average temperature change rate, extreme precipitation event probability) and human interference factors (including industrial pollution emission intensity, construction land expansion rate) are screened, and the risk value of each factor is quantified through GIS spatial analysis methods; S42, ecological risk model construction and weight allocation: Through the spatial overlay analysis method, the screened natural disasters, climate change and human interference factor layers are weighted and superimposed with the ecological service function level and ecological sensitivity level layers, and the weight of each factor is determined (natural disaster weight 0.35, climate change weight 0.30, human interference weight 0.35), and an ecological risk index model is constructed to calculate the comprehensive ecological risk index of each region.

[0015] S43, Risk level division and dynamic threshold setting: Based on the ecological risk index value, the target area is divided into high-risk area, medium-risk area and low-risk area using the natural breakpoint method; S44, Dynamic early warning mechanism design: Based on real-time remote sensing image data, climate data and human activity data, a time series risk prediction model is established. The model dynamically calculates the changing trend of the ecological risk index in the next 30 days, and sets four levels of early warning signals and response strategies based on the risk index.

[0016] S45, early warning output and feedback linkage: the generated risk level, early warning signal and response strategy are pushed in real time through the GIS management platform to generate an early warning report. At the same time, the dynamic monitoring and adjustment mechanism described in S5 is triggered to prioritize the optimization and adjustment of the ecological protection red line boundaries in high-risk areas to further reduce potential ecological risks.

[0017] Optionally, the S44 includes: S441, data preprocessing and feature extraction: perform radiation correction and cloud mask processing on the collected real-time remote sensing image data, extract vegetation index, surface temperature and water index, perform temporal interpolation on climate data (such as precipitation, wind speed, temperature), fill in missing values, generate spatial continuous raster data, convert human activity data into spatial resolution data that matches the ecological risk index model, and ensure that all types of data have consistent spatial resolution and time span; S442, construction of time series risk prediction model: Based on historical ecological risk index data and real-time monitoring data (real-time remote sensing image data, climate data and human activity data), a long short-term memory network is used to construct a time series risk prediction model. The input features of the model include the current risk index, vegetation index change rate, precipitation deviation and human activity intensity increment. The output is the risk index forecast value for the next 30 days.

[0018] S443, adaptive adjustment of dynamic warning thresholds: set differentiated warning thresholds based on the divided ecological service function levels.

[0019] S444, multi-level warning signal generation and strategy binding: Generate four-level warning signals based on the predicted risk index and adaptively adjusted thresholds.

[0020] S445, early warning visualization: superimpose the generated early warning signal with the vector layer generated in S35, use heat map rendering to display the risk level distribution, and push it to the supervision terminal (including PC and mobile devices) in real time.

[0021] Optionally, the S5 includes: S51, multi-source real-time data integration and feature coding: Based on the collected real-time remote sensing images, climate data, and human activity distribution data, the vegetation coverage change rate, surface temperature anomaly, construction land expansion rate, and pollution source distribution characteristics are extracted. At the same time, the generated four-level risk warning signals, ecological service function level, and ecological sensitivity level are connected and encoded into a multi-dimensional feature vector aligned in time and space, and input into the red line adjustment decision model; S52, Dynamic adjustment model construction and training: Use deep reinforcement learning framework to build a redline adjustment decision model.

[0022] S53, priority-driven adjustment strategy generation: setting adjustment priorities based on a combination of risk warning levels and ecological service function levels.

[0023] Combined with the control rules of S34 (such as the prohibition of shrinkage in the first-level control area), a set of candidate adjustment plans is generated, and plans that conflict with the control rules are eliminated; S54, spatial optimization and conflict resolution of redline boundaries: GIS spatial analysis is performed on the candidate adjustment scheme set, and a multi-objective genetic algorithm is used to optimize the redline boundary. The optimization objectives include: maximizing the connectivity of ecological corridors (calculated by the landscape index MSPA), minimizing the area of ​​conflict with human activities (refer to the conflict area in S32), and maximizing the protection ratio of high-function areas (≥80%). Finally, the Pareto optimal solution set is output, and the optimal redline adjustment scheme is selected after manual review; S55, adjustment effect feedback and model iteration: synchronize the optimal red line adjustment plan to the management platform in real time, and monitor the changes in the ecological risk index and the stability of the functional level within 30 days after the adjustment through the early warning feedback mechanism.

[0024] Beneficial effects of the present invention: This invention, through comprehensive ecological basic data collection (such as vegetation coverage, soil erosion intensity, climate data, etc.), combined with a variety of ecological models (such as InVEST model, CASA model, RUSLE model), conducts ecological function assessment and classification, which can scientifically and systematically assess the ecological service function, sensitivity and stability of the target area. This multi-level and multi-dimensional assessment method makes the delineation of ecological protection red lines more accurate, ensures the maximization of ecological functions of ecological protection areas, and effectively avoids the risk of over-development, thereby improving the scientificity and accuracy of ecological protection.

[0025] The present invention constructs an accurate ecological risk assessment model based on the dynamic monitoring and early warning mechanism of real-time data (such as remote sensing images, meteorological monitoring, climate change, etc.), and combines deep learning technology to predict the risk index and adjust the dynamic threshold. Through the four-level early warning signal and the emergency response strategy matching the risk level, it is possible to capture the changes in regional ecological risks in a timely manner and make dynamic adjustments, effectively improving the response speed and flexibility of ecological protection. In addition, combined with the dynamic red line adjustment decision model, the red line boundary can be automatically optimized according to the risk changes and ecological function assessment results, ensuring that the ecological protection red line is always in a reasonable and effective state under the new ecological risk environment, and maximizing ecological security.

[0026] The present invention, by using a multi-objective genetic algorithm to resolve conflicts in the red line boundary optimization, can balance the conflict between ecological protection needs and human activities (such as construction, industry, agriculture, etc.), maximize the connectivity of ecological corridors, minimize the conflict area of ​​human activities, and ensure the protection ratio of high-function areas. Through this optimization process, the adverse effects of human activities on the ecological environment can be effectively avoided, and the coordination of ecological protection and regional sustainable development can be promoted. In particular, the priority protection of highly sensitive areas ensures the scientificity and effectiveness of the ecological protection red line area, providing a reliable guarantee for the long-term stability and healthy development of the ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention; Figure 2 Schematic diagram of S3 process of an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0030] like Figure 1-Figure 2 As shown, a method for demarcating ecological protection red lines applied to ecological basic surveys includes the following steps: S1, Collection of ecological basic data: Collect ecological basic data in the target area, including vegetation coverage, soil erosion intensity, surface water distribution, land use status data, climate data, human activity distribution data, historical disaster records and remote sensing image data, and perform spatial matching and standardization processing through geographic information system; S2, Ecological function assessment and classification: Based on the collected ecological basic data, ecological models are used to assess the stability of ecological units, the intensity of ecological service functions (water conservation, carbon sink, soil conservation) and ecological sensitivity, and to classify ecological service function levels (high, medium, low); S3, Regional ecological function division and redline framework design: Based on the ecological service function level, combined with the current land use status and the boundaries of nature reserves, the ecological protection redline area is delineated, and the protection objectives and development restriction types are clarified; S4, Ecological risk assessment and early warning: Based on the assessment results of S2, an ecological risk model is constructed to assess the risks of natural disasters, climate change and human interference factors in the red line area, and a dynamic early warning mechanism is designed; S5, dynamic monitoring and adjustment mechanism: using the real-time ecological basic data of S1, combining the ecological service function level and risk warning level through machine learning algorithms, dynamically adjust the red line range to respond to the risk warning results of S4.

[0031] S1 includes: S11, multi-source data acquisition and classification: obtain multi-temporal and multi-spectral remote sensing image data of the target area through satellite remote sensing platforms, collect vegetation coverage, soil erosion intensity, surface water distribution and climate data (including precipitation, temperature, wind speed) through ground monitoring stations, obtain land use status data (including the spatial distribution and area proportion of cultivated land, forest land, grassland, construction land, and unused land) through satellite remote sensing interpretation and land survey databases, obtain human activity distribution data (including the spatial distribution of residential areas, industrial land, and transportation networks) through social and economic statistics databases and field surveys, and obtain historical disaster records (including the time, spatial distribution and impact range of floods, landslides, mud-rock flows and other events) through local disaster management department databases and historical remote sensing image inversion; S12, data preprocessing and format standardization: perform radiation correction, atmospheric correction and geometric precision correction on remote sensing image data to generate standard false color synthetic images, perform missing value filling, outlier removal and time series smoothing on ground monitoring data and human activity data, and uniformly convert them into vector or raster formats (such as Shapefile, GeoTIFF) compatible with geographic information systems (GIS); S13, unification of spatial reference system: all data are uniformly projected into the same geographic coordinate system (such as WGS84 coordinate system), and a uniform spatial resolution (such as 30m × 30m grid) is set, and data scale differences are eliminated through resampling, spatial interpolation and vector-to-raster conversion; S14, spatial matching and attribute association of ecological data: superimpose vegetation coverage, soil erosion intensity, surface water distribution, climate data and human activity distribution data on the GIS platform, perform layer registration based on spatial location, establish an attribute association table, and achieve one-to-one mapping between ecological parameters and geographic units (e.g., each grid unit is associated with vegetation type, erosion level, water source distance, climate factors and human activity intensity); S15, data quality verification and integrated storage: ensure data accuracy through cross-validation (such as comparison of remote sensing data with ground-measured data) and spatial consistency verification (such as smooth transition of data in adjacent areas), and finally integrate the standardized multi-source ecological data into the spatiotemporal database, and store them by timestamp and spatial block classification to support the dynamic call of subsequent ecological function assessment models.

[0032] S2 includes: S21, quantification of ecological service function indicators: Based on the ecological basic data collected in S1, the water conservation module in the InVEST model is used to calculate the water conservation capacity, the net primary productivity carbon sink module of the CASA model is used to evaluate the carbon sink intensity, and the soil erosion modulus calculation module of the RUSLE equation is used to evaluate the soil conservation intensity; The water conservation module of the InVEST model uses the following calculation method to estimate the water conservation capacity, which is calculated as: ; in, is the water conservation capacity, P is the precipitation, is the evapotranspiration coefficient, E is the evapotranspiration, and R is the runoff; The CASA model is used to assess the net primary productivity (NPP) of plants in a region, and then the carbon sink intensity is calculated based on the NPP, expressed as: NPP calculation formula: ; Where NPP is net primary productivity, f(T,P,I) is the plant growth model function, which is usually related to factors such as temperature T, precipitation P and light I, and A is the area of ​​the region; The carbon sink is estimated based on NPP and is expressed as: ;in, is the carbon sink intensity; The RUSLE equation estimates soil erosion and calculates soil conservation strength by the difference between potential erosion and actual erosion. The RUSLE equation is expressed as: ; in, is the amount of soil erosion, R is the precipitation erosion factor, K is the soil erodibility factor, LS is the topographic factor (including slope and slope length), C is the cover factor (related to vegetation type and land cover), is the soil conservation measures factor (reflecting artificial measures, such as soil and water conservation projects); S22, normalization of functional intensity and weight allocation: normalize the calculated water conservation, carbon sink intensity and soil conservation intensity. Specifically, a linear normalization formula is used to map the minimum value of each service function indicator to 0 and the maximum value to 1. Then, the analytic hierarchy process is used in combination with expert scoring to determine the weight of each function, where the weight of water conservation is set to 0.4, the weight of carbon sink is 0.3, and the weight of soil conservation is 0.3. Finally, the normalized functional intensity values ​​are weighted and summed to obtain the comprehensive ecological service function intensity value of each ecological unit. The specific calculation formula is: ; in, , , are the normalized intensity values ​​of water conservation, carbon sink and soil conservation, respectively. , , is the corresponding weight value; S23, service function level division: according to the comprehensive ecological service function intensity value, the target area is divided into three levels: high, medium and low using the natural breakpoint method; Specifically, if the comprehensive service function strength value of a certain area , then the area is classified as a high-function area; if It is divided into medium functional areas. , it is divided into low-function areas. The results of the grading are stored in the form of a raster layer, and the attribute value of each raster unit is associated with the spatial boundary of its corresponding ecological unit to form a complete spatial distribution map. This division result not only facilitates the subsequent red line framework design, but also provides clear decision-making support for ecological protection and regional planning.

[0033] S24, screening and quantification of ecological sensitivity factors: Based on the ecological basic data collected in S1, terrain slope, vegetation coverage, soil erosion intensity, surface water proximity and human activity disturbance intensity were selected as ecological sensitivity factors, and the quantitative values ​​of ecological sensitivity factors were calculated by GIS spatial analysis method, where: Slope: graded according to the terrain slope: 0-5° is grade 1, 5-15° is grade 2, and >15° is grade 3; Vegetation coverage: The normalized difference vegetation index (NDVI) value is used for segmentation: NDVI < 0.2 is highly sensitive, 0.2 ≤ NDVI < 0.5 is moderately sensitive, and NDVI ≥ 0.5 is lowly sensitive; Soil erosion intensity: graded according to the soil erosion intensity value calculated by the RUSLE model; Proximity to surface water: Classified according to the distance between each ecological unit and the surface water body: <1km from the water body is highly sensitive, 1-5km is medium sensitive, and >5km is low sensitive; Intensity of interference from human activities: Classified according to the distance from residential areas or industrial land: distance <1km is highly sensitive, 1-5km is medium sensitive, and >5km is low sensitive.

[0034] S25, Sensitivity index integration and classification: Define the weight of each sensitivity factor, where the slope weight is 0.25, the vegetation coverage weight is 0.30, the soil erosion intensity weight is 0.20, the surface water proximity weight is 0.15, and the human activity interference intensity weight is 0.10. By weighted superposition of the quantitative values ​​of each factor, an ecological sensitivity index layer is generated, and the sensitivity index is divided into three levels: high, medium, and low using the equal spacing method: Highly sensitive area: index ≥ 0.75; Medium sensitive area: 0.5≤index<0.75; Low sensitivity area: index <0.5.

[0035] S26, Ecosystem Stability Assessment: Based on the ecological service function intensity level obtained in S23 and the ecological sensitivity level obtained in S25, an ecosystem stability matrix is ​​constructed, defining: The “high function-low sensitivity” area is level one in stability (priority protection); The “medium function-medium sensitivity” area and the “high function-medium sensitivity” area are level 2 stability (moderate protection); The “low function-high sensitivity” area and the “medium function-high sensitivity” area are at level 3 stability (repair priority); Generate a spatial distribution map of stability levels, associate the distribution map with the attribute table of the ecological unit, and finally output the stability assessment results of the ecosystem.

[0036] S3 includes: S31, Identification of ecological priority protection areas: Based on the divided ecological service function levels (high, medium, and low) and ecological sensitivity levels (high, medium, and low), the "high function-low sensitivity" and "high function-medium sensitivity" areas are screened out as priority protection areas through GIS spatial overlay analysis methods, and preliminary ecological protection red line candidate areas are delineated in combination with the boundaries of existing nature reserves. The specific method is: using spatial reclassification technology, the ecological function and sensitivity level layers are superimposed to identify areas that meet the protection conditions; S32, land use conflict analysis and boundary optimization: spatially overlay the land use data collected in S1 with the candidate red line areas to identify areas where human activity conflicts exist, adjust the conflicting areas, and optimize the boundaries of the affected areas based on the principle of ecological function priority, such as shrinking the scope of industrial land and preserving the integrity of ecological corridors, to ensure that the ecological functions and ecological connectivity of the ecological protection red line areas are not destroyed.

[0037] S33, Fusion and expansion of protected area boundaries: spatially integrate the existing nature reserve boundaries with the optimized red line candidate areas, and analyze whether there are areas with high ecological service functions (such as water conservation areas, important carbon sink areas, etc.) outside the nature reserve that are not included. If so, use buffer zone analysis (radius ≥ 1km) to expand the unincluded areas and include them in the red line protection scope to form a more coherent ecological protection network; S34, Protection objectives and development restriction classification: Based on the combination of ecological service function level and ecological sensitivity level within the red line area, three levels of control rules are defined. The specific rules are as follows: Level 1 control area (high function-low sensitivity area): All development activities are prohibited in this area, and only ecological research and monitoring are allowed; Secondary control area (high function-medium sensitivity / medium function-low sensitivity area): infrastructure construction is restricted in this area, and the construction of polluting industries is prohibited; Level 3 control area (medium function-medium sensitivity area): Moderate ecotourism and low-intensity agricultural activities are allowed in this area, but large-scale development projects are prohibited.

[0038] S35, redline area mapping and attribute association: Through GIS mapping technology, the ecological protection redline area and its corresponding control rules are finally encoded into a vector layer, and the detailed attribute table fields are associated, including: functional level, sensitivity level, control type, regional area ratio, etc. Finally, a standard geographic information format file (such as GeoJSON, Shapefile, etc.) is generated, and the redline area distribution map, control rule table and spatial conflict optimization report are output for decision-making and subsequent management.

[0039] S4 includes: S41, risk factor screening and quantification: Based on the divided ecological service function level and the generated ecological sensitivity level, combined with the climate data, human activity distribution data and historical disaster records collected in S1, natural disaster factors (including flood frequency, landslide susceptibility index), climate change factors (including annual average temperature change rate, extreme precipitation event probability) and human interference factors (including industrial pollution emission intensity, construction land expansion rate) are screened, and the risk value of each factor is quantified through GIS spatial analysis methods; Specifically, flood frequency is graded by the number of historical occurrences, pollution emission intensity is quantified by the amount of pollutants emitted per unit area, landslide susceptibility is calculated by factors such as slope and precipitation, and the probability of extreme precipitation events is calculated based on historical meteorological data; S42, Ecological risk model construction and weight allocation: Through the spatial overlay analysis method, the selected natural disaster, climate change and human interference factor layers are weighted and superimposed with the ecological service function level and ecological sensitivity level layers, and the weight of each factor is determined (natural disaster weight 0.35, climate change weight 0.30, human interference weight 0.35), and the ecological risk index model is constructed to calculate the comprehensive ecological risk index of each region. The formula is as follows: Ecological risk index = ∑(factor value × weight); Generate a spatial distribution map of ecological risk index in the target area and associate it with the spatial boundaries of ecological units.

[0040] S43, Risk level division and dynamic threshold setting: Based on the ecological risk index value, the target area is divided into high-risk area, medium-risk area and low-risk area using the natural breakpoint method, as follows: high-risk area (index ≥ 0.8); Medium risk area (0.5≤index<0.8); Low risk area (index < 0.5); On this basis, the threshold is dynamically adjusted according to the level of ecological service function. For example, for areas with higher ecological functions (such as water conservation areas), the risk threshold is reduced by 20% to improve the early warning sensitivity of the area and ensure timely detection of risks; S44, Dynamic early warning mechanism design: Based on real-time remote sensing image data, climate data and human activity data, a time series risk prediction model is established. The model dynamically calculates the trend of ecological risk index changes in the next 30 days, and sets four-level early warning signals and response strategies according to the risk index. Red alert (index ≥ 0.8): extremely high risk, initiate emergency response immediately.

[0041] Orange warning (0.6≤index<0.8): high risk, some protective measures are initiated.

[0042] Yellow warning (0.4≤index<0.6): medium risk, enhanced supervision and protection measures.

[0043] Blue warning (index <0.4): low risk, maintain routine management.

[0044] And link early warning signals with emergency response strategies (such as suspending all development activities when a red alert occurs) to ensure timely response.

[0045] S45, early warning output and feedback linkage: The generated risk level, early warning signal and response strategy are pushed in real time through the GIS management platform to generate an early warning report, which includes detailed information such as risk type, spatial location, and response measures. At the same time, the dynamic monitoring and adjustment mechanism of S5 is triggered to optimize and adjust the ecological protection red line boundaries of high-risk areas first, further reducing potential ecological risks.

[0046] S44 includes: S441, data preprocessing and feature extraction: perform radiation correction and cloud mask processing on the collected real-time remote sensing image data, extract vegetation index, surface temperature and water index, perform temporal interpolation on climate data (such as precipitation, wind speed, temperature), fill in missing values, generate spatial continuous raster data, and convert human activity data (such as the expansion scope of construction land and the location of pollution sources) into spatial resolution data that matches the ecological risk index model, ensuring that all types of data have consistent spatial resolution and time span for subsequent analysis and prediction; S442, construction of time series risk prediction model: Based on historical ecological risk index data and real-time monitoring data (real-time remote sensing image data, climate data and human activity data), a long short-term memory network is used to construct a time series risk prediction model. The input features of the model include the current risk index, vegetation index change rate, precipitation deviation and human activity intensity increment. The output is the risk index forecast value for the next 30 days. The training data set is divided into training set, validation set and test set in a ratio of 7:2:1. Early stopping is applied during training to prevent overfitting. The Adam optimizer is selected as the optimizer, and the mean square error is used as the loss function to ensure model stability and prediction accuracy.

[0047] S443, adaptive adjustment of dynamic warning thresholds: differentiated warning thresholds are set according to the ecological service function levels. Specifically, the red warning threshold for high-function areas is set to the value of the top 90% of the historical risk index data, that is, if the current risk index reaches or exceeds the maximum value of the top 90% of the past data, a red warning is triggered, the red warning threshold for medium-function areas is set to the value of the top 85% of the historical risk index data, and the red warning threshold for low-function areas is set to the value of the top 80% of the historical risk index data. When real-time meteorological data triggers an extreme climate event (for example, precipitation exceeds 100 mm within 24 hours), the warning threshold will be temporarily reduced by 10% to increase the sensitivity of the warning, thereby ensuring that sudden ecological risks can be responded to in a timely manner.

[0048] S444, multi-level warning signal generation and strategy binding: Based on the predicted risk index and the adaptively adjusted threshold, a four-level warning signal is generated: Red warning (≥ threshold); Orange warning (threshold × 0.8 ≤ index < threshold); Yellow warning (threshold × 0.6 ≤ index < threshold × 0.8); Blue warning (<threshold × 0.6); Each warning signal is bound to a corresponding emergency response strategy. For example, a red warning triggers "suspending construction permit approval in the red line area", and an orange warning triggers "restricting production of polluting enterprises by 50%". Specific strategies are managed through the GIS management platform and can be manually revised and optimized according to real-time needs.

[0049] S445, early warning visualization: superimpose the generated early warning signal with the vector layer generated in S35, use heat map rendering to display the risk level distribution, and push it to the supervision terminal (including PC and mobile devices) in real time.

[0050] S5 includes: S51, multi-source real-time data integration and feature coding: Based on the collected real-time remote sensing images, climate data, and human activity distribution data, the vegetation coverage change rate, surface temperature anomaly, construction land expansion rate, and pollution source distribution characteristics are extracted. At the same time, the generated four-level risk warning signals, ecological service function level, and ecological sensitivity level are connected and encoded into a multi-dimensional feature vector aligned in time and space, and input into the red line adjustment decision model; S52, Dynamic adjustment model construction and training: A deep reinforcement learning framework is used to construct a redline adjustment decision model, in which the state space includes the ecological service function level, risk warning level and human activity intensity of the current redline area, and the action space is the expansion, contraction or maintenance of the boundary (such as expansion of 500 meters or contraction of 200 meters). The reward function is weighted calculation based on the ecological function improvement value and risk reduction value (weight ratio 6:4). The model training uses historical adjustment records (≥3 years of data) and iterates the optimization strategy through the Q-learning algorithm until convergence to ensure the ecological protection effect of the redline area.

[0051] S53, priority-driven adjustment strategy generation: setting adjustment priorities based on the combination of risk warning level and ecological service function level; Red warning + high-function areas are given priority (forced boundary expansion). Orange warning + high-function areas are secondary priority (extension is recommended). Yellow warning + medium functional areas are level three priority (local optimization).

[0052] Combined with the control rules of S34 (such as the prohibition of shrinkage in the first-level control area), a set of candidate adjustment plans is generated, and plans that conflict with the control rules are eliminated; S54, spatial optimization and conflict resolution of redline boundaries: GIS spatial analysis is performed on the candidate adjustment scheme set, and a multi-objective genetic algorithm is used to optimize the redline boundary. The optimization objectives include: maximizing the connectivity of ecological corridors (calculated by the landscape index MSPA), minimizing the area of ​​conflict with human activities (refer to the conflict area in S32), and maximizing the protection ratio of high-function areas (≥80%). Finally, the Pareto optimal solution set is output, and the optimal redline adjustment scheme is selected after manual review; S55, adjustment effect feedback and model iteration: synchronize the optimal red line adjustment plan to the management platform in real time, and monitor the changes in the ecological risk index and the stability of the functional level within 30 days after the adjustment through the early warning feedback mechanism. If the risk does not decrease or the functional level is reduced, trigger model retraining (update the reward function weight or expand the state characteristics), iterate the model parameters once a month and record them in the spatiotemporal database, thus forming a closed-loop mechanism of "monitoring → adjustment → verification → optimization".

[0053] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0054] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for demarcating ecological protection red lines for ecological basic surveys, characterized in that: The following steps are involved: S1, Collection of ecological basic data: Collect ecological basic data in the target area, including vegetation coverage, soil erosion intensity, surface water distribution, land use status data, climate data, human activity distribution data, historical disaster records and remote sensing image data, and perform spatial matching and standardization processing through geographic information system; S2, Ecological function assessment and classification: Based on the collected ecological basic data, ecological models are used to assess the stability, ecological service function intensity and ecological sensitivity of ecological units, and to classify ecological service function levels; S3, Regional ecological function division and redline framework design: Based on the ecological service function level, combined with the current land use status and the boundaries of nature reserves, the ecological protection redline area is delineated, and the protection objectives and development restriction types are clarified; S4, Ecological risk assessment and early warning: Based on the assessment results of S2, an ecological risk model is constructed to assess the risks of natural disasters, climate change and human interference factors in the red line area, and a dynamic early warning mechanism is designed; S5, dynamic monitoring and adjustment mechanism: using the real-time ecological basic data of S1, combining the ecological service function level and risk warning level through machine learning algorithms, dynamically adjust the red line range to respond to the risk warning results of S4.

2. The method for demarcating ecological protection red lines for ecological basic survey according to claim 1 is characterized in that: The S1 includes: S11, multi-source data acquisition and classification: obtain multi-temporal and multi-spectral remote sensing image data of the target area through satellite remote sensing platforms, collect vegetation coverage, soil erosion intensity, surface water distribution and climate data through ground monitoring stations, obtain land use status data through satellite remote sensing interpretation and land survey databases, obtain human activity distribution data through social and economic statistics databases and field surveys, and obtain historical disaster records through local disaster management department databases and historical remote sensing image inversion; S12, data preprocessing and format standardization: perform radiation correction, atmospheric correction and geometric precision correction on remote sensing image data to generate standard false color synthetic images, perform missing value filling, outlier removal and time series smoothing on vegetation coverage, soil erosion intensity, surface water distribution, climate data and human activity data, and uniformly convert them into vector or raster formats compatible with geographic information systems; S13, spatial reference system unification: all data are uniformly projected into the same geographic coordinate system, and a uniform spatial resolution is set, eliminating data scale differences through resampling, spatial interpolation, and vector-to-raster conversion; S14, spatial matching and attribute association of ecological data: superimpose vegetation coverage, soil erosion intensity, surface water distribution, climate data and human activity distribution data on the GIS platform, perform layer registration based on spatial location, establish an attribute association table, and achieve one-to-one mapping between ecological parameters and geographic units; S15, data quality verification and integrated storage: ensure data accuracy through cross-validation and spatial consistency testing, and finally integrate the standardized ecological basic data into the database.

3. The method for demarcating ecological protection red lines for ecological basic survey according to claim 2 is characterized in that: The S2 includes: S21, quantification of ecological service function indicators: Based on the ecological basic data collected in S1, the water conservation module in the InVEST model is used to calculate the water conservation capacity, the net primary productivity carbon sink module of the CASA model is used to evaluate the carbon sink intensity, and the soil erosion modulus calculation module of the RUSLE equation is used to evaluate the soil conservation intensity; S22, normalization of functional intensity and weight allocation: normalize the calculated water conservation capacity, carbon sink intensity and soil conservation intensity, use the analytic hierarchy process combined with expert scoring to determine the weight of each function, and perform weighted summation of the normalized functional intensity values ​​to obtain the comprehensive ecological service functional intensity value of each ecological unit; S23, service function level division: according to the comprehensive ecological service function intensity value, the target area is divided into three levels: high, medium and low using the natural breakpoint method.

4. The method for demarcating ecological protection red lines for ecological basic survey according to claim 3 is characterized in that: The S2 further includes: S24, screening and quantification of ecological sensitivity factors: Based on the ecological basic data collected in S1, terrain slope, vegetation coverage, soil erosion intensity, surface water proximity and human activity disturbance intensity were selected as ecological sensitivity factors, and the quantitative values ​​of ecological sensitivity factors were calculated through GIS spatial analysis methods; S25, sensitivity index integration and classification: define the weight of each sensitivity factor, among which the slope weight is 0.25, the vegetation coverage weight is 0.30, the soil erosion intensity weight is 0.20, the surface water proximity weight is 0.15, and the human activity interference intensity weight is 0.

10. By weighted superposition of the quantitative values ​​of each factor, an ecological sensitivity index layer is generated, and the sensitivity index is divided into three levels: high, medium, and low using the equal spacing method; S26, ecosystem stability assessment: Based on the ecological service function intensity level obtained in S23 and the ecological sensitivity level obtained in S25, an ecosystem stability matrix is ​​constructed, and finally the ecosystem stability assessment results are output.

5. The method for demarcating ecological protection red lines for ecological basic survey according to claim 4 is characterized in that: The S3 includes: S31, Identification of ecological priority protection areas: Based on the ecological service function level and ecological sensitivity level, the "high function-low sensitivity" and "high function-medium sensitivity" areas are selected as priority protection areas through GIS spatial overlay analysis method, and the preliminary ecological protection red line candidate areas are delineated in combination with the boundaries of existing nature reserves; S32, land use conflict analysis and boundary optimization: spatially overlay the land use status data collected in S1 with the candidate red line areas, identify areas with human activity conflicts, adjust the conflicting areas, and optimize the boundaries of the affected areas based on the principle of ecological function priority; S33, fusion and expansion of protected area boundaries: spatially integrate the existing nature reserve boundaries with the optimized red line candidate areas, and analyze whether there are areas with high ecological service functions outside the nature reserve that have not been included; S34, Protection objectives and development restriction classification: Define three levels of control rules based on the combination of ecological service function level and ecological sensitivity level within the red line area; S35, red line area mapping and attribute association: Through GIS mapping technology, the final ecological protection red line area and its corresponding management and control rules are encoded into a vector layer and associated with the attribute table fields.

6. The method for demarcating ecological protection red lines for ecological basic survey according to claim 5 is characterized in that: The S4 includes: S41, risk factor screening and quantification: Based on the divided ecological service function level and the generated ecological sensitivity level, combined with the climate data, human activity distribution data and historical disaster records collected in S1, natural disaster factors, climate change factors and human interference factors are screened, and the risk value of each factor is quantified through GIS spatial analysis methods; S42, Ecological risk model construction and weight allocation: Through the spatial overlay analysis method, the selected natural disaster, climate change and human interference factor layers are weighted and superimposed with the ecological service function level and ecological sensitivity level layers, and the weight of each factor is determined to construct an ecological risk index model and calculate the comprehensive ecological risk index of each region; S43, Risk level division and dynamic threshold setting: Based on the ecological risk index value, the target area is divided into high-risk area, medium-risk area and low-risk area using the natural breakpoint method; S44, Dynamic early warning mechanism design: Based on real-time remote sensing image data, climate data and human activity data, a time series risk prediction model is established. The model dynamically calculates the trend of ecological risk index changes in the next 30 days and sets four-level early warning signals and response strategies based on the risk index; S45, early warning output and feedback linkage: the generated risk level, early warning signal and response strategy are pushed in real time through the GIS management platform to generate an early warning report. At the same time, the dynamic monitoring and adjustment mechanism described in S5 is triggered to prioritize the optimization and adjustment of the ecological protection red line boundaries in high-risk areas.

7. The method for demarcating ecological protection red lines for ecological basic survey according to claim 6 is characterized in that: The S44 includes: S441, data preprocessing and feature extraction: perform radiation correction and cloud mask processing on the collected real-time remote sensing image data, extract vegetation index, surface temperature and water index, perform temporal interpolation on climate data, fill in missing values, generate spatially continuous raster data, and convert human activity data into spatial resolution data that matches the ecological risk index model; S442, Construction of time series risk prediction model: Based on historical ecological risk index data and real-time monitoring data, a time series risk prediction model is constructed using a long short-term memory network. The input features of the model include the current risk index, vegetation index change rate, precipitation deviation, and human activity intensity increment. The output is the risk index forecast value for the next 30 days. S443, adaptive adjustment of dynamic warning thresholds: setting differentiated warning thresholds according to the divided ecological service function levels; S444, multi-level warning signal generation and strategy binding: Generate four-level warning signals based on the predicted risk index and the adaptively adjusted threshold; S445, early warning visualization: superimpose the generated early warning signal with the vector layer generated in S35, use heat map rendering to display the risk level distribution, and push it to the supervision terminal in real time.

8. The method for demarcating ecological protection red lines for ecological basic survey according to claim 7 is characterized in that: The S5 includes: S51, multi-source real-time data integration and feature coding: Based on the collected real-time remote sensing images, climate data, and human activity distribution data, the vegetation coverage change rate, surface temperature anomaly, construction land expansion rate, and pollution source distribution characteristics are extracted. At the same time, the generated four-level risk warning signals, ecological service function level, and ecological sensitivity level are connected and encoded into a multi-dimensional feature vector aligned in time and space, and input into the red line adjustment decision model; S52, Dynamic Adjustment Model Construction and Training: Use deep reinforcement learning framework to build a redline adjustment decision model; S53, priority-driven adjustment strategy generation: according to the combination of risk warning level and ecological service function level, the adjustment priority is set, and combined with the control rules of S34, a set of candidate adjustment plans is generated, and plans that conflict with the control rules are eliminated; S54, Spatial optimization and conflict resolution of redline boundaries: GIS spatial analysis is performed on the candidate adjustment scheme set, and a multi-objective genetic algorithm is used to optimize the redline boundary. The optimization objectives include: maximizing the connectivity of ecological corridors, minimizing the conflict area with human activities, and maximizing the protection ratio of high-function areas. Finally, the Pareto optimal solution set is output, and the optimal redline adjustment scheme is selected after manual review; S55, adjustment effect feedback and model iteration: synchronize the optimal red line adjustment plan to the management platform in real time, and monitor the changes in the ecological risk index and the stability of the functional level within 30 days after the adjustment through the early warning feedback mechanism.

Images