A method for basic investigation of ecological protection red line based on territorial space planning

Abstract

The application relates to the technical field of ecological protection, in particular to a method for investigating ecological protection red line based on land space planning, which comprises the following steps: through data collection, including satellite remote sensing, unmanned aerial vehicle aerial survey and ground sensor and the like, high-precision spatial data of ecological elements such as vegetation coverage, surface water distribution and soil erosion intensity are obtained. In combination with time-space feature matching and an improved spatial clustering algorithm, ecological elements are dynamically partitioned, and an ecological factor dynamic partitioning map is generated. Based on the map, a three-dimensional ecological sensitivity evaluation model is constructed, and a dynamic threshold adjustment algorithm is adopted to automatically optimize the red line boundary. Finally, through multidimensional verification and evaluation of natural ecological stability, regional economic carrying capacity, social acceptance and the like, a final ecological protection red line scheme is generated. The application improves the adaptability and flexibility of red line demarcation, and can be widely applied to the fields of ecological protection, land space planning, environmental management and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological protection, and in particular to a basic investigation method for ecological protection red lines based on land space planning. BACKGROUND

[0002] In order to effectively protect the ecological environment and ensure ecological safety, a series of ecological protection policies have been introduced, one of the most critical measures being the delineation of ecological protection red lines. Ecological protection red lines refer to ecological spaces that are prohibited from being destroyed and must be protected, which are delineated on the basis of scientific assessment. The purpose is to ensure the integrity and functionality of the ecological system through the protection of important ecosystems, ecological functions and species habitats, thereby providing protection for sustainable development.

[0003] Currently, existing methods for delineating ecological protection red lines rely heavily on traditional ecological data collection methods such as ground surveys and limited remote sensing data, lacking sufficient support from multiple sources. These methods typically collect data with a coarse granularity and low update frequency, making it difficult to comprehensively and accurately analyze the spatial distribution and temporal dynamics of ecological factors. In addition, traditional delineation methods also have strong human intervention, often relying on fixed rules or experience to delineate red line boundaries, which is easily influenced by human factors and lacks sufficient scientificity and objectivity, which may lead to overprotection or insufficient protection. In terms of multi-dimensional comprehensive assessment, existing technologies have not fully considered the mutual relationship between ecological, economic and social factors. Traditional methods focus on ecological protection, while ignoring the influence of regional economic carrying capacity and social acceptance, leading to ecological protection measures that may greatly restrict local economic activities. SUMMARY

[0004] The present application provides a basic investigation method for ecological protection red lines based on land space planning.

[0005] A basic investigation method for ecological protection red lines based on land space planning, comprising the following steps:

[0006] S1, data collection: synchronously acquiring ecological element spatial data including vegetation coverage, surface water distribution and soil erosion intensity through satellite remote sensing, unmanned aerial vehicle aerial survey and ground sensors;

[0007] S2, dynamic zoning of ecological elements: after matching the spatial and temporal characteristics of the acquired ecological element spatial data, combining with the terrain relief factor, an improved spatial clustering algorithm is used to generate an ecological factor dynamic zoning map;

[0008] S3, three-dimensional sensitivity modeling: based on the ecological factor dynamic zoning map of S2, superimposing an elevation gradient correction coefficient, constructing a three-dimensional ecological sensitivity evaluation model to evaluate the sensitivity of the ecological system, and outputting the three-dimensional sensitivity evaluation result;

[0009] S4, red line dynamic demarcation: according to the three-dimensional sensitivity evaluation result output by S3, a dynamic threshold adjustment algorithm is used to demarcate the boundary of the ecological protection red line, and a preliminary ecological protection red line scheme is generated;

[0010] S5, multi-dimensional verification and evaluation: the preliminary ecological protection red line scheme of S4 is subjected to three-dimensional verification and evaluation of natural ecological stability, regional economic carrying capacity and social acceptance, and a final ecological protection red line scheme is generated.

[0011] Optionally, S1 comprises:

[0012] S11, satellite remote sensing data collection: collecting spatial data of vegetation coverage, surface water distribution and soil erosion intensity in the target area through satellite remote sensing equipment;

[0013] S12, unmanned aerial vehicle aerial survey data collection: using an unmanned aerial vehicle to carry high-precision sensors for aerial survey to obtain high-precision three-dimensional spatial data of vegetation coverage, surface water distribution and soil erosion intensity in the region.

[0014] S13, ground sensor data collection: laying ground sensors in the target area for synchronous data collection;

[0015] S14, synchronous data space-time matching and fusion: matching and fusing satellite remote sensing, unmanned aerial vehicle aerial survey and ground sensor data obtained in S11, S12 and S13 in time and space to ensure that data from different sources can be accurately connected. Through data alignment, coordinate transformation and other methods, data of different resolutions and formats are integrated into a unified three-dimensional spatial data layer for subsequent analysis.

[0016] Optionally, S2 comprises:

[0017] S21, space-time feature matching: performing space-time feature matching on the collected vegetation coverage, surface water distribution and soil erosion intensity data;

[0018] S22, terrain undulation factor calculation: calculating the terrain undulation factor of the region according to the digital elevation model (DEM) of the region.

[0019] S23, application of clustering algorithm: using an improved K-means algorithm to perform clustering analysis on the data in steps S21 and S22.

[0020] Optionally, S2 further comprises:

[0021] S24, dynamic zoning map generation: generating an ecological factor dynamic zoning map according to the clustering analysis result;

[0022] S25, verification and optimization of the zoning map: the generated dynamic ecological factor zoning map is verified and optimized.

[0023] Optionally, the S3 comprises:

[0024] S31, dynamic ecological factor zoning map: a generated dynamic ecological factor zoning map is received, which has been spatially clustered according to the factors of vegetation coverage, surface water distribution, soil erosion intensity, and terrain relief. Each zone represents an area with similar ecological characteristics and contains relevant ecological factor data, including the distribution range and intensity of vegetation coverage, the spatial pattern of surface water distribution, and the spatial variation of soil erosion intensity;

[0025] S32, calculation of elevation gradient correction coefficient: according to the digital elevation model (DEM) of the target area, the elevation gradient correction coefficient of each spatial unit is calculated;

[0026] S33, construction of three-dimensional ecological sensitivity evaluation model: based on the ecological factor data and the terrain elevation correction coefficient, a three-dimensional ecological sensitivity evaluation model is constructed.

[0027] S34, output of three-dimensional sensitivity evaluation result: according to the constructed three-dimensional ecological sensitivity evaluation model, the sensitivity of the target area's ecosystem is evaluated, and the three-dimensional sensitivity evaluation result is output, and a three-dimensional sensitivity evaluation result map is generated.

[0028] Optionally, the S3 further comprises:

[0029] S35, sensitivity level division: according to the ecological sensitivity index in the three-dimensional sensitivity evaluation result map, the area is divided into several sensitivity levels;

[0030] S36, spatial distribution display: the areas of each sensitivity level are visualized as three-dimensional maps of different colors or shades, making it easy to understand the spatial sensitivity distribution of the ecosystem;

[0031] S37, result verification and optimization: the generated three-dimensional ecological sensitivity evaluation result is verified and optimized.

[0032] Optionally, the S4 comprises:

[0033] S41, receiving three-dimensional sensitivity evaluation result: receiving the generated three-dimensional sensitivity evaluation result, which includes the ecological sensitivity index of each spatial unit, reflecting the ecological vulnerability of each region.

[0034] S42, determination of red line boundary threshold: according to the ecological protection target and red line demarcation requirements of the region, a preliminary red line boundary threshold is set.

[0035] S43, dynamic threshold adjustment algorithm: a dynamic threshold adjustment algorithm is used to optimize the preliminarily set red line boundary;

[0036] S44, red line boundary generation and preliminary scheme output: according to the optimized red line boundary, a preliminary ecological protection red line scheme is generated.

[0037] S45, preliminary red line scheme review and optimization: the generated preliminary ecological protection red line scheme is reviewed and optimized by experts to ensure that the delineation result meets the ecological protection target.

[0038] Optionally, the S5 comprises:

[0039] S51, natural ecological stability evaluation: the preliminary ecological protection red line scheme is evaluated for natural ecological stability, and the natural ecological stability evaluation aims to verify whether the ecological system of the ecological protection red line region can maintain long-term stability and self-repairing ability.

[0040] S52, regional economic carrying capacity evaluation: the preliminary ecological protection red line scheme is evaluated for regional economic carrying capacity, and the regional economic carrying capacity evaluation is used to evaluate whether the economic activities in the red line region will be restricted by the red line boundary and whether the economic development and ecological protection can be balanced.

[0041] S53, social acceptance evaluation: the preliminary ecological protection red line scheme is evaluated for social acceptance, and the social acceptance evaluation aims to determine the social support degree of the red line delineation region and evaluate the recognition degree of the social groups (such as local residents, enterprises, policy makers, etc.) to the red line boundary delineation.

[0042] S54, three-dimensional comprehensive evaluation and optimization: the evaluation results of natural ecological stability, regional economic carrying capacity and social acceptance are comprehensively analyzed, and the final ecological protection red line scheme is output.

[0043] The beneficial effects of the present application are:

[0044] The present application ensures that the ecological protection red line demarcation has high precision and scientificity through the steps of data acquisition, ecological factor dynamic partitioning, three-dimensional ecological sensitivity modeling and red line dynamic demarcation. Specifically, first, satellite remote sensing, unmanned aerial vehicle aerial survey and ground sensor are used to synchronously collect core ecological element data such as vegetation coverage, surface water distribution and soil erosion intensity, so as to obtain high-precision ecological element spatial information. Then, the ecological element data are subjected to time-space feature matching and partitioning in combination with terrain factors and an improved spatial clustering algorithm, and the ecological sensitivity is subjected to quantitative analysis through a three-dimensional ecological sensitivity evaluation model, so as to provide strong data support for accurate red line demarcation, and the red line boundary is subjected to optimization through a dynamic adjustment algorithm, so that the red line demarcation is more in line with the ecological protection target, and the accuracy and reliability of the red line demarcation result are ensured, thereby providing a feasible technical basis for ecological protection.

[0045] The present application introduces a multi-dimensional verification and evaluation mechanism after the red line demarcation, comprehensively considers factors such as natural ecological stability, regional economic carrying capacity and social acceptance, etc. Through natural ecological stability evaluation, it is ensured that the demarcated red line region has sufficient ecological self-repairing capacity and long-term stability, and imbalance and degradation of the ecological system are avoided. Through regional economic carrying capacity evaluation, the demand for ecological protection and economic development is reasonably balanced, and excessive restriction of ecological protection measures on regional economy is prevented. Through social acceptance evaluation, it is ensured that the red line scheme is widely supported and cooperated by all sectors of society in the actual implementation process, and the feasibility and implementation success rate of the scheme are improved. This multi-dimensional comprehensive evaluation system makes the red line demarcation not only consider the demand for ecological protection, but also take into account the economic sustainable development and the actual situation of society, and truly realizes the coordination and balance of ecology, economy and society. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0047] Fig. 1 The method flowchart of the embodiment of the present application is shown in the figure.

[0048] Fig. 2 The S3 flowchart of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] The application will be described in detail below in conjunction with the drawings and specific embodiments. It should be noted here that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the application.

[0050] As shown in Figs. 1-2 A method for basic investigation of ecological protection red line based on territorial space planning, comprising the following steps:

[0051] S1, data acquisition: acquiring ecological element spatial data including vegetation coverage, surface water distribution and soil erosion intensity through satellite remote sensing, unmanned aerial vehicle aerial survey and ground sensor synchronization;

[0052] S2, dynamic zoning of ecological elements: after matching the spatial and temporal characteristics of the acquired ecological element spatial data, combining with the terrain relief factor, an improved spatial clustering algorithm is used to generate an ecological factor dynamic zoning map;

[0053] S3, three-dimensional sensitivity modeling: based on the ecological factor dynamic zoning map of S2, superimposing the elevation gradient correction coefficient, constructing a three-dimensional ecological sensitivity evaluation model to evaluate the sensitivity of the ecological system, and outputting the three-dimensional sensitivity evaluation result;

[0054] S4, dynamic red line delineation: according to the three-dimensional sensitivity evaluation result output by S3, using a dynamic threshold adjustment algorithm to delineate the ecological protection red line boundary, and generating a preliminary ecological protection red line scheme;

[0055] S5, multidimensional verification and evaluation: performing three-dimensional verification and evaluation of the preliminary ecological protection red line scheme of S4 in terms of natural ecological stability, regional economic carrying capacity and social acceptance, and generating a final ecological protection red line scheme.

[0056] S1 includes:

[0057] S11, satellite remote sensing data acquisition: acquiring spatial data of vegetation coverage, surface water distribution and soil erosion intensity in the target area through satellite remote sensing equipment, using remote sensing sensors (such as optical sensors, radar sensors, etc.) to obtain high-resolution remote sensing images, according to the collected images, using image processing methods to extract vegetation index (such as NDVI) to estimate vegetation coverage, using multi-band remote sensing data to calculate surface water distribution, and combining soil surface characteristics of remote sensing images to evaluate soil erosion intensity;

[0058] S12, unmanned aerial vehicle aerial survey data acquisition: using a high-precision sensor carried by an unmanned aerial vehicle to perform aerial survey, and acquiring high-precision three-dimensional spatial data of vegetation coverage, surface water distribution and soil erosion intensity in the region;

[0059] When conducting aerial survey with UAV, the flight path, flight height and image shooting angle are designed according to the regional characteristics to ensure the accuracy and coverage of the data. After collection, the images are geometrically corrected and radiometrically corrected, and specific ecological element data is extracted through image analysis.

[0060] S13, ground sensor data collection: ground sensors are deployed in the target area for synchronous data collection, specifically including:

[0061] Vegetation coverage measurement: real-time monitoring of vegetation coverage is performed through ground spectral sensors or infrared sensors to obtain accurate spatial distribution data.

[0062] Surface water distribution: soil moisture sensors, weather sensors, etc. are used to monitor the water distribution of the soil surface and vegetation surface in real time.

[0063] Soil erosion intensity monitoring: soil erosion intensity sensors are deployed to monitor the erosion of water flow, wind erosion, etc. on the soil, and soil erosion intensity data is collected;

[0064] S14, spatiotemporal matching and fusion of synchronous data: satellite remote sensing, UAV aerial survey and ground sensor data obtained in S11, S12 and S13 are matched and fused in time and space to ensure accurate connection of data from different sources. Through data alignment, coordinate transformation, etc., data of different resolutions and formats are integrated into a unified three-dimensional spatial data layer for subsequent analysis.

[0065] S2 includes:

[0066] S21, spatiotemporal feature matching: the collected vegetation coverage, surface water distribution and soil erosion intensity data are matched in time and space. First, each type of data is standardized in time stamp to ensure consistency in time of data collected at different times. Second, through spatial alignment methods such as coordinate transformation and projection transformation, data from different platforms (satellites, UAVs, ground sensors) are converted into a unified spatial coordinate system. Finally, through spatial resolution unification algorithms such as interpolation algorithms, spatial resolution matching of different data sources is performed to ensure that all data are analyzed on the same spatial scale;

[0067] S22, terrain relief factor calculation: according to the digital elevation model (DEM) of the region, the terrain relief factor of the region is calculated;

[0068] Firstly, the slope, aspect, and elevation parameters of the terrain are extracted from the DEM data to obtain the terrain characteristics of each pixel point. Then, according to the principles of ecology, the relationship between the terrain characteristics and the ecological elements is analyzed, and the terrain relief factor is defined and assigned to each spatial unit (pixel or grid). The terrain relief factor will be used as an important weight factor in spatial clustering analysis, reflecting the influence of terrain on the distribution of ecological elements.

[0069] S23, clustering algorithm application: the improved K-means algorithm is used to perform clustering analysis on the data in steps S21 and S22;

[0070] Compared with the traditional K-means algorithm, the improved K-means algorithm introduces ecological factors (vegetation coverage, surface water distribution, soil erosion intensity) and terrain relief factors (slope, aspect, etc.) for joint weighting, enhancing the adaptability of the algorithm to complex ecological regions. The specific operation steps of the improved K-means algorithm are as follows:

[0071] (1) Initialization stage: first, calculate the feature vector of each data point according to the ecological factors (vegetation coverage, surface water distribution, soil erosion intensity) and the terrain relief factor. The feature vector includes:

[0072] The numerical values of vegetation coverage, surface water distribution, and soil erosion intensity (after standardization).

[0073] The terrain relief factors, such as slope and aspect, are processed using the same standardization method as the ecological factor data.

[0074] Randomly select K initial cluster centers, and the value of K is set reasonably according to the geographical characteristics and ecological diversity of the target region.

[0075] (2) Clustering calculation stage:

[0076] Distance calculation: for each data point, calculate the distance between its feature vector and the K cluster centers. The distance metric uses weighted Euclidean distance (or other suitable distance metric methods), and the weighting coefficients are set according to the relative importance of each ecological factor and terrain factor.

[0077] For example, if the influence weight of vegetation coverage on ecological zoning is high, the weight of vegetation coverage can be set to a large value; the terrain relief factor is given appropriate weight according to the influence of different terrains.

[0078] Redistribution of data points: assign each data point to the nearest cluster center to generate K clusters.

[0079] (3) Update cluster center stage:

[0080] For each data point within a cluster, the weighted mean of the cluster is calculated, and the location of the cluster center is updated. In the weighted mean calculation, the weights of each ecological factor and terrain factor are considered to ensure that the new cluster center can better represent the comprehensive ecological characteristics within the cluster.

[0081] The clustering and updating steps are iterated until the cluster centers no longer change significantly or the maximum number of iterations is reached.

[0082] (4) Cluster result output: K joint weighted clustering regions of ecological factors and terrain factors are finally obtained. Each region represents an ecological division with similar ecological and terrain characteristics. The clustering results reflect the comprehensive differences in vegetation coverage, surface water distribution, soil erosion intensity, and terrain characteristics in different regions.

[0083] The output clustering results will serve as the basis for subsequent ecological protection redline delineation and generate a preliminary ecological factor dynamic zoning map.

[0084] S2 also includes:

[0085] S24, dynamic zoning map generation: according to the clustering analysis results, an ecological factor dynamic zoning map is generated. Each clustering region will be labeled with its main ecological characteristics, such as the average or range of vegetation coverage, water distribution, and soil erosion intensity. This zoning map will provide the basis for subsequent ecological protection redline delineation and ecological sensitivity assessment. At the same time, the generated dynamic zoning map is used to analyze the variation of different ecological factors under different terrain conditions;

[0086] S25, zoning map verification and optimization: the generated ecological factor dynamic zoning map is verified and optimized;

[0087] Firstly, by comparing with actual ecological data (such as field survey results, historical ecological data, etc.), the accuracy of the zoning is verified. Secondly, according to the stability of the ecological environment, the distribution of biodiversity within the region, and other standards, the zoning map is adjusted and optimized to ensure that the generated zoning has high ecological significance and protection value.

[0088] S3 includes:

[0089] S31, ecological factor dynamic zoning map: receive the generated ecological factor dynamic zoning map, which has been spatially clustered according to vegetation coverage, surface water distribution, soil erosion intensity, and terrain relief factor. Each division represents a region with similar ecological characteristics and contains relevant ecological factor data, including the distribution range and intensity of vegetation coverage, the spatial pattern of surface water distribution, and the spatial variation of soil erosion intensity;

[0090] S32, Elevation Gradient Correction Coefficient Calculation: Calculate the elevation gradient correction coefficient for each spatial unit based on the digital elevation model (DEM) of the target area. The specific steps include:

[0091] Calculate the elevation difference: Extract the elevation value of each pixel point from the DEM, calculate the elevation difference between adjacent pixels, and obtain the elevation gradient;

[0092] Elevation gradient correction: According to the influence of terrain on ecological sensitivity, set the elevation gradient correction coefficient. For example, in areas with large slope, soil erosion may be more serious, so its elevation correction coefficient can be appropriately increased;

[0093] Correction coefficient distribution: Assign the correction coefficient to each spatial unit in the ecological factor dynamic zoning map to obtain a dataset with elevation correction coefficients;

[0094] S33, Construction of Three-dimensional Ecological Sensitivity Evaluation Model: Based on ecological factor data and terrain elevation correction coefficients, construct a three-dimensional ecological sensitivity evaluation model. The basic principle of the three-dimensional ecological sensitivity evaluation model is to combine ecological factor data and terrain elevation correction coefficients to evaluate the ecological sensitivity of each region. The specific steps are as follows:

[0095] Model input: Input the ecological factor data (vegetation coverage, surface water distribution, soil erosion intensity) and elevation gradient correction coefficient of each spatial unit.

[0096] Sensitivity calculation: Use a weighted comprehensive model to give different weights to different ecological factors and elevation correction coefficients, and perform weighted calculation according to their influence on ecological system sensitivity.

[0097] For example, the reduction of vegetation coverage may lead to an increase in ecological sensitivity, so the influence weight of vegetation coverage is larger; while the relationship between soil erosion intensity and terrain relief factor will affect the distribution of correction coefficients.

[0098] Calculate the sensitivity index: Calculate the ecological sensitivity index of each region by weighted summation method to obtain a three-dimensional sensitivity data layer. Each pixel of this layer represents the ecological sensitivity of that location, and the larger the value represents the higher the sensitivity of the ecological system.

[0099] S34, Three-dimensional Sensitivity Evaluation Result Output: According to the constructed three-dimensional ecological sensitivity evaluation model, evaluate the sensitivity of the target area's ecological system, output the three-dimensional sensitivity evaluation result, and generate a three-dimensional sensitivity evaluation result map. This map shows the sensitivity index of each region and can be visualized as different color scales or contour lines to intuitively display the spatial distribution of ecological sensitivity within the region.

[0100] S3 also includes:

[0101] S35, Sensitivity Level Classification: Based on the ecological sensitivity index in the three-dimensional sensitivity evaluation result map, the region is divided into several sensitivity levels, and the classification example is as follows:

[0102] Low sensitivity: sensitivity index < 0.4;

[0103] Medium sensitivity: 0.4 ≤ sensitivity index < 0.6;

[0104] High sensitivity: 0.6 ≤ sensitivity index < 0.8;

[0105] Very high sensitivity: sensitivity index > 0.8;

[0106] These thresholds are only for reference, and should be adjusted in combination with the actual situation of the target area and the statistical distribution of the ecological sensitivity index in actual use. For example:

[0107] In some areas with relatively fragile ecological environment, more conservative thresholds may be used, i.e. above 0.6 is considered high sensitivity.

[0108] In a more robust ecological system, the threshold can be appropriately relaxed;

[0109] Suppose there are three ecological factors: vegetation coverage, soil erosion intensity and surface moisture, and the corresponding standardized values are 0.8, 0.6 and 0.7, and the weights of each factor are:

[0110] Vegetation coverage weight 0.4;

[0111] Soil erosion intensity weight 0.3;

[0112] Surface moisture weight 0.3;

[0113] Sensitivity index: sensitivity index = (0.8 x 0.4) + (0.6 x 0.3) + (0.7 x 0.3) = 0.32 + 0.18 + 0.21 = 0.71

[0114] The sensitivity index of this region is 0.71, indicating that the region is at a high level of ecological sensitivity;

[0115] S36, Spatial distribution display: visualize the regions of each sensitivity level as three-dimensional maps of different colors or shades, to facilitate intuitive understanding of the spatial sensitivity distribution of the ecological system;

[0116] S37, Result verification and optimization: verify and optimize the generated three-dimensional ecological sensitivity evaluation results, the specific steps include:

[0117] Verification: verify the accuracy of the generated ecological sensitivity results through field investigation, historical ecological data and expert evaluation, etc.

[0118] Optimization: Based on the verification results, adjust the model parameters, optimize the weighting coefficients or model algorithms, and further improve the accuracy and applicability of the evaluation results.

[0119] S4 includes:

[0120] S41, receiving three-dimensional sensitivity evaluation results: receiving the generated three-dimensional sensitivity evaluation results, the results including the ecological sensitivity index of each spatial unit, which reflects the ecological vulnerability of each region. The result can be a three-dimensional raster data set representing the sensitivity value of each grid, ranging from 0 (low sensitivity) to 1 (extremely high sensitivity).

[0121] S42, determining the red line boundary threshold: according to the ecological protection target and red line demarcation requirements of the region, set the preliminary red line boundary threshold. The selection of threshold is based on the following factors:

[0122] Ecological protection needs: set the threshold according to the regional ecological protection target. If the goal is to protect areas with higher ecological sensitivity, a lower threshold can be selected, and vice versa, if the goal is to protect areas with lower sensitivity, a higher threshold can be selected.

[0123] Policy requirements: determine the demarcation standard of ecological protection red line in combination with relevant ecological protection regulations.

[0124] Three-dimensional sensitivity evaluation results: according to the three-dimensional sensitivity evaluation results, dynamically adjust the threshold. Specifically, the red line threshold can be appropriately lowered in areas with higher sensitivity index, and the threshold can be increased in areas with lower sensitivity.

[0125] S43, dynamic threshold adjustment algorithm: use the dynamic threshold adjustment algorithm to optimize the preliminary red line boundary, making the red line demarcation result more accurate, the specific steps are as follows:

[0126] Preliminary red line setting: according to the threshold setting, first classify the three-dimensional sensitivity evaluation results, divide the region into different sensitivity levels. For high sensitivity areas (such as sensitivity index higher than a certain threshold), preliminary red line area is demarcated.

[0127] Threshold dynamic adjustment: according to the following standards, dynamically adjust the preliminary demarcated red line boundary:

[0128] Ecological feature similarity adjustment: if the ecological features of adjacent regions are similar (such as sensitivity index close), the red line area can be appropriately expanded or contracted to ensure the rationality of the red line.

[0129] Terrain Factor Adjustment: Considering the impact of terrain factors on the ecological environment, such as areas with higher slope may face higher risk of soil erosion, the red line boundary should be adjusted according to the correction coefficient of slope or elevation gradient.

[0130] Spatial Clustering Analysis Adjustment: Through spatial clustering analysis, the ecological sensitivity data within the region is re-clustered, further optimizing the red line boundary to avoid overly scattered red line delineation.

[0131] Automatic Boundary Optimization: Through simulated annealing algorithm, the red line boundary is further adjusted to make the red line area more in line with the needs of ecological protection, while avoiding unreasonable boundary cutting.

[0132] The steps of simulated annealing algorithm to optimize the red line boundary are as follows:

[0133] (1) Define the optimization objective function:

[0134] First, a target function needs to be defined as a standard for evaluating the optimization effect of the red line boundary. The target function usually considers the following factors:

[0135] Maximize Ecological Protection Value: Ensure that the red line covers areas with high ecological sensitivity, i.e., protect as much as possible the areas with fragile and sensitive ecosystems.

[0136] Regional Connectivity: The optimized red line boundary should avoid fragmentation as much as possible, ensuring good connectivity between ecological protection areas, avoiding isolated ecological protection areas.

[0137] Reasonable Red Line Boundary: The red line boundary should as much as possible conform to the actual terrain and ecological system characteristics, avoiding unnatural cutting.

[0138] The target function can be expressed as:

[0139] Ecological Value Connectivity Reasonableness;

[0140] Where, , , are the weights of the corresponding factors, ensuring a reasonable balance between the three in the optimization process.

[0141] (2) Initialize the solution space and state representation:

[0142] The initial solution of the red line boundary is obtained through the set preliminary red line scheme, which can be represented by gridding, where each grid cell's state represents whether the cell belongs to the red line area (for example, 1 represents that the grid belongs to the red line, 0 represents not).

[0143] The initial solution space is all possible red line boundary combinations, which can be optimized through neighborhood search. Each search variation produces a new red line boundary solution.

[0144] (3) Set the temperature and iteration process of simulated annealing:

[0145] Simulated annealing algorithm simulates the annealing process of physical systems, gradually reduces the "temperature", and gradually transitions the system from the initial state to the global optimal solution.

[0146] (3.1) Initial temperature setting: Set a high initial temperature (e.g. initial temperature 1000) according to the complexity of the problem. High temperature helps the algorithm jump out of local optimal solution and try different boundary demarcation schemes.

[0147] (3.2) Temperature decay: Control the search range by gradually reducing the temperature. The slower the temperature decreases, the more refined the search process, and the final convergence to the global optimal solution. The temperature decay function is:

[0148] ;

[0149] Where, is the current temperature, is the decay factor, usually taking values from 0.9 to 0.99;

[0150] (3.3) Acceptance criterion: In each iteration, the algorithm generates a new candidate solution and calculates its objective function value. If the new solution is better than the current solution, accept the solution; if the new solution is worse, accept it according to the temperature acceptance probability, which is:

[0151] ;

[0152] If the solution is accepted, update the current solution, otherwise, continue to keep the current solution;

[0153] (4) Neighborhood search and solution adjustment:

[0154] In each iteration process, the simulated annealing algorithm performs neighborhood search on the current solution, that is, it generates a new candidate solution by slightly adjusting the current red line boundary. Common neighborhood search methods include:

[0155] Local area expansion / contraction: Expand or contract part of the red line boundary to increase or decrease the protected area.

[0156] Red line boundary smoothing: Smooth the boundary to avoid too tortuous boundary and improve its naturalness.

[0157] Merge / split area: Merge adjacent red line areas or split larger red line areas to ensure the connectivity of the area.

[0158] (5) Stopping criteria and output of optimal solution: The simulated annealing algorithm will stop iterating when the temperature drops to a certain level, or after reaching the maximum number of iterations. The final output solution is the optimized red line boundary;

[0159] S44, red line boundary generation and preliminary scheme output: Based on the optimized red line boundary, a preliminary ecological protection red line scheme is generated, with the following specific steps:

[0160] Red line area demarcation: According to the adjusted sensitivity index threshold, the boundary of the red line is finally determined, and the ecological protection red line area is marked. This area is usually the area with high ecological sensitivity that needs to be protected first.

[0161] Preliminary red line scheme output: Generate a preliminary red line scheme containing the red line boundary. This scheme can be visualized as a spatial distribution map of the red line, indicating the specific location, boundary and contained ecological elements of the red line.

[0162] S45, preliminary red line scheme review and optimization: The generated preliminary ecological protection red line scheme is reviewed and optimized by experts to ensure that the demarcation result meets the ecological protection goal. The specific steps include:

[0163] Expert review: The preliminary red line scheme is reviewed by the expert group to assess whether it is reasonable and meets the requirements of ecological protection.

[0164] Adjustment and optimization: According to the results of expert review, further optimize the red line boundary. Possible optimization directions include: adjusting the boundary according to ecological data and expert suggestions, and correcting the red line demarcated areas with problems.

[0165] S5 includes:

[0166] S51, natural ecological stability assessment: The natural ecological stability of the preliminary ecological protection red line scheme is assessed, which aims to verify whether the ecological system of the ecological protection red line area can maintain long-term stability and self-repairing ability. The specific steps are as follows:

[0167] Ecosystem health assessment: Use vegetation coverage, soil erosion intensity and surface water data to calculate the ecosystem health index of the red line area, and assess the overall stability and recovery ability of the ecosystem. Higher vegetation coverage and good water distribution usually mean a healthier ecosystem.

[0168] Ecological vulnerability identification: Identify ecologically vulnerable places in the red line area, such as areas with high soil erosion intensity and insufficient vegetation coverage. Calculate the proportion of these vulnerable areas in the red line area, and identify potential ecological risk areas through spatial analysis.

[0169] S52, Regional Economic Carrying Capacity Assessment: Assess the preliminary ecological protection red line scheme for regional economic carrying capacity. Regional economic carrying capacity assessment is used to assess whether the economic activities in the red line area will be restricted by the red line boundary, and whether the balance between economic development and ecological protection can be achieved. The specific steps are as follows:

[0170] Land use status analysis: Through obtaining land use data in the region, analyze the current land use types of the red line area, including agriculture, industry, residential area, etc. According to the land use status, assess the density and spatial layout of economic activities in the red line area.

[0171] Economic activity impact analysis: Assess the potential impact of economic activities (such as agricultural production, infrastructure construction, etc.) in the red line area on the ecological environment, focusing on areas with sensitive ecological environment, and judging the possible negative impact of economic activities on the ecological protection red line area.

[0172] Carrying capacity calculation: Through the economic carrying capacity model, combined with regional GDP, per capita income and other economic data, calculate the economic carrying capacity of the red line area. This model can assess the carrying capacity of regional economic activities, ensuring the coordination between economy and ecological protection.

[0173] S53, Social Acceptance Assessment: Assess the preliminary ecological protection red line scheme for social acceptance. Social acceptance assessment aims to determine the social support for red line demarcation, and assess the recognition degree of social groups (such as local residents, enterprises, policymakers, etc.) to the red line boundary demarcation. The specific steps are as follows:

[0174] Public opinion collection: Through questionnaire survey, symposium and other forms, widely collect public opinions on red line demarcation. The survey content includes the ecological value of red line demarcation area, the influence on economic development, the influence on residents' life, etc.

[0175] Social and economic impact assessment: Analyze the impact of red line demarcation on local economy, residents' living standard and social employment. For example, assess the loss of farmland, land expropriation problems, restrictions on infrastructure construction, etc. caused by red line demarcation.

[0176] S54, Three-dimensional comprehensive assessment and optimization: Comprehensive analysis of the assessment results of natural ecological stability, regional economic carrying capacity and social acceptance, output the final ecological protection red line scheme. The specific steps are as follows:

[0177] Weighted comprehensive assessment: According to the assessment results of different dimensions, set the weight (according to the priority of ecological protection goal), and weighted sum the assessment results of three aspects. For example, the weight of ecological stability may be larger, and the weight of social acceptance may be smaller.

[0178] Optimization and adjustment: according to the comprehensive evaluation results, judge whether the preliminary red line scheme needs to be adjusted. If the ecological stability of a certain area is poor, the economic carrying capacity is too high, or the social acceptance is low, consider adjusting the red line boundary, reducing the delineation of these areas, and increasing other areas that meet the ecological protection requirements.

[0179] Final red line scheme output: through the optimization of the adjusted red line scheme, output the final ecological protection red line scheme. The scheme should achieve a balance between ecological protection, economic development and social acceptance, ensuring feasibility and effectiveness.

[0180] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0181] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A basic survey method for ecological protection red lines based on national land space planning, characterized by: The following steps are involved: S1, Data Collection: Using satellite remote sensing, drone aerial surveys, and ground sensors to simultaneously acquire spatial data on ecological elements, including vegetation cover, surface water distribution, and soil erosion intensity; S2, dynamic zoning of ecological factors: After matching the spatial and temporal characteristics of the acquired ecological factor spatial data, an improved spatial clustering algorithm is used to generate a dynamic zoning map of ecological factors in combination with the terrain relief factor; S3, 3D sensitivity modeling: Based on the dynamic zoning map of ecological factors in S2, the elevation gradient correction coefficient is superimposed to construct a 3D ecological sensitivity assessment model to evaluate the sensitivity of the ecosystem and output the 3D sensitivity assessment results, including: S31, dynamic zoning map of ecological factors: Based on the generated dynamic zoning map of ecological factors, extract the relevant ecological factor data in the dynamic zoning map of ecological factors, including the distribution range and intensity of vegetation coverage, the spatial pattern of surface water distribution, and the spatial variation of soil erosion intensity; S32, elevation gradient correction coefficient calculation: Calculate the elevation gradient correction coefficient of each spatial unit based on the digital elevation model of the target area; S33, constructing a three-dimensional ecological sensitivity assessment model: constructing a three-dimensional ecological sensitivity assessment model based on ecological factor data and terrain elevation correction coefficients; S34, output of three-dimensional sensitivity evaluation results: based on the constructed three-dimensional ecological sensitivity evaluation model, the sensitivity of the ecosystem in the target area is evaluated, the three-dimensional sensitivity evaluation results are output, and a three-dimensional sensitivity evaluation result map is generated; S35, Sensitivity level division: Based on the ecological sensitivity index in the three-dimensional sensitivity assessment result map, the area is divided into several sensitivity levels; S36, spatial distribution display: visualize each sensitivity level area as a three-dimensional map with different colors or shadows; S37, Result Verification and Optimization: Verify and optimize the generated three-dimensional ecological sensitivity assessment results; S4, dynamic redline delineation: Based on the three-dimensional sensitivity assessment results output by S3, a dynamic threshold adjustment algorithm is used to delineate the ecological protection redline boundary and generate a preliminary ecological protection redline plan; S5, multi-dimensional verification and evaluation: Conduct a three-dimensional verification and evaluation of the preliminary ecological protection red line plan of S4 in terms of natural ecological stability, regional economic carrying capacity, and social acceptance to generate the final ecological protection red line plan.

2. The basic survey method for ecological protection red lines based on national land space planning according to claim 1 is characterized in that: Said S1 comprises: S11, satellite remote sensing data collection: collect spatial data on vegetation coverage, surface water distribution and soil erosion intensity in the target area through satellite remote sensing equipment; S12, UAV aerial survey data collection: Use drones equipped with sensors to conduct aerial surveys to obtain three-dimensional spatial data on vegetation coverage, surface water distribution, and soil erosion intensity in the area; S13, ground sensor data collection: deploying ground sensors in the target area to perform synchronous data collection; S14, synchronous data spatiotemporal matching and fusion: Match and fuse the satellite remote sensing, UAV aerial survey and ground sensor data obtained in S11, S12 and S13 in time and space, and integrate data of different resolutions and formats into a unified three-dimensional spatial data layer.

3. The basic survey method for ecological protection red lines based on national land space planning according to claim 2 is characterized in that: The S2 includes: S21, temporal and spatial feature matching: performing temporal and spatial feature matching on the collected vegetation coverage, surface water distribution and soil erosion intensity data; S22, terrain relief factor calculation: Calculate the terrain relief factor of the region based on the digital elevation model of the region; S23, clustering algorithm application: using an improved K-means algorithm to perform cluster analysis on the data in steps S21 and S22.

4. The basic survey method for ecological protection red lines based on national land space planning according to claim 3 is characterized in that: Said S2 further comprises: S24, dynamic partition map generation: Generate a dynamic partition map of ecological factors based on the cluster analysis results; S25, Partition map verification and optimization: Verify and optimize the generated dynamic partition map of ecological factors.

5. The basic survey method for ecological protection red lines based on national land space planning according to claim 4 is characterized in that: The S4 includes: S41, receiving a three-dimensional sensitivity evaluation result: receiving a generated three-dimensional sensitivity evaluation result, the result including an ecological sensitivity index of each spatial unit; S42, determine the red line boundary threshold: set the preliminary red line boundary threshold according to the regional ecological protection goals and red line delineation requirements; S43, dynamic threshold adjustment algorithm: using a dynamic threshold adjustment algorithm to optimize the initially set red line boundary; S44, Redline boundary generation and preliminary plan output: Generate a preliminary ecological protection redline plan based on the optimized redline boundary; S45, Review and optimization of preliminary red line plan: Expert review and optimization of the generated preliminary ecological protection red line plan.

6. The basic survey method for ecological protection red lines based on national land space planning according to claim 5 is characterized in that: The S5 includes: S51, Natural Ecological Stability Assessment: Conduct a natural ecological stability assessment on the preliminary ecological protection redline plan. The natural ecological stability assessment aims to verify whether the ecosystem in the ecological protection redline area can maintain long-term stability and self-repair capabilities; S52, Regional Economic Carrying Capacity Assessment: Conduct a regional economic carrying capacity assessment on the preliminary ecological protection red line plan. The regional economic carrying capacity assessment is used to assess whether economic activities within the red line area will be restricted by the red line boundary; S53, Social Acceptance Assessment: Conduct a social acceptance assessment of the preliminary ecological protection redline plan. The social acceptance assessment aims to determine the social support for the redline area and assess the degree of recognition of the redline boundary demarcation by social groups; S54, three-dimensional comprehensive assessment and optimization: Comprehensively analyze the assessment results of natural ecological stability, regional economic carrying capacity, and social acceptance, and output the final ecological protection red line plan.

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