A dual-level selection method for terrestrial ecological protection red line identification points

Abstract

The present invention provides a two-level selection method for terrestrial ecological protection red line identification points, which relates to the field of geographic information technology and comprises the following steps: S1, collecting terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data, and performing data preprocessing on all collected data; S2, calculating the preprocessed data to obtain basic indicators, and performing normalization on the basic indicators; S3, calculating the human activity intensity classification, ecological protection demand classification, and walking suitability classification according to the basic indicators, and calculating the boundary stake point priority and the signboard point priority; S4, selecting boundary stake points in combination with the boundary stake point priority and the ecological protection red line inflection point importance level to obtain boundary stake point recognition results, and selecting signboard candidate points in combination with the signboard point priority and the signboard candidate point level to obtain signboard point recognition results.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a double-level selection method for terrestrial ecological protection red line identification points. Background Art

[0002] The ecological protection red line refers to an area within the scope of ecological space that has special and important ecological functions and must be strictly protected. It is the bottom line and lifeline for ensuring and maintaining national ecological security, including the terrestrial ecological protection red line and the marine ecological protection red line. In order to promote the implementation of the ecological protection red line and implement strict management and protection, it is necessary to carry out the calibration of the terrestrial ecological protection red line, that is, to set up unified and standardized ecological protection red line boundary stakes and signboards at important points. Boundary stakes refer to the boundary marker stakes set up in a certain way along the boundary of the ecological protection red line; signboards refer to signboards set up in conspicuous locations for the purpose of warning and publicity, containing basic information such as the name, area, scope, function, and supervision of the ecological protection red line. The establishment of boundary stakes and signboards needs to comprehensively consider effectiveness and feasibility, and achieve effective control of the ecological protection red line as much as possible within the limited cost control range. Therefore, effective technical methods must be adopted to accurately identify the optimal location of boundary stakes and signboards to ensure the economic and social benefits of this work.

[0003] The Technical Regulations for the Demarcation and Marking of the Ecological Protection Red Line (Environmental Protection Office Ecology

[2019] No. 49, hereinafter referred to as the Regulations) stipulate that "based on the principle of controlling the basic direction of the boundary line, boundary stakes shall be buried at key control points such as key sections and important turning points", and "based on the goal of warning and publicizing the ecological protection red line, taking full account of the terrain, landmarks, landforms and population distribution characteristics, signboards shall be buried in conspicuous locations such as areas that are easily accessible and where human activities are relatively dense or at the intersections of roads and red lines". Among them, "key sections" refer to major intersections, village peripheries and other places where human activities are concentrated; "important turning points" refer to points where the direction of the ecological protection red line boundary changes significantly. However, the Regulations use qualitative descriptions for the location determination methods of boundary stakes and signboards, and do not specify specific quantifiable data processing and boundary stake and signboard coordinate determination rules.

[0004] According to the Regulations, the traditional determination of the locations of boundary stakes and signboards relies on manual experience, which has low work efficiency, high randomness in spatial positioning, and low accuracy, which is not conducive to the large-scale development of ecological protection red line calibration work and the scientific management of ecological protection red lines. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides a two-level selection method for terrestrial ecological protection red line identification points to solve the above-mentioned technical problems.

[0006] The technical method adopted by the present invention to solve its technical problems is: a two-level selection method for terrestrial ecological protection red line identification points, and the improvement thereof is that it includes the following steps: S1, collecting terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data according to specific geometric characteristics and scale requirements, and performing data preprocessing on all collected data; S2, calculating the preprocessed data to obtain basic indicators, and normalizing the basic indicators; S3, calculating the human activity intensity classification, ecological protection demand classification, and walking suitability classification according to the basic indicators, and then calculating the boundary stake point priority according to the human activity intensity classification and the ecological protection demand classification, and calculating the signboard point priority according to the human activity intensity classification and the walking suitability classification; S4, selecting the boundary stake point in combination with the boundary stake point priority and the ecological protection red line inflection point importance level, and carrying out morphological deviation correction to obtain the boundary stake point recognition result, and selecting the signboard candidate point in combination with the signboard point priority and the signboard candidate point level, and carrying out aggregation point judgment to obtain the signboard point recognition result.

[0007] In the above method, in step S1, according to specific geometric features and scale requirements, collecting terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data includes the following steps:

[0008] S11. Collect the terrestrial ecological redline data for point selection, using a common vector space data format, with the geometric features being lines and a scale of no less than 1:10000;

[0009] S12. Collect data on human activity intensity, including building data, daily average number of mobile phone users, and night light remote sensing satellite data;

[0010] S13. Collect ecological and environmental protection evaluation data for the entire city, including the ecological function importance and ecological and environmental sensitivity evaluation results data generated during the delineation of the ecological protection red line, using a common raster spatial data format with a spatial resolution of no less than 200m and a scale of no less than 1:10000;

[0011] S14. Collect the data of walkable roads in the whole city. Walkable roads include municipal roads, park roads and greenways. The data adopts the universal vector space data format. The geometric features are lines. The scale is not less than 1:2000. All walkable roads include width attributes. Municipal roads include road type attributes. Road type attributes include expressways, primary and secondary roads and branch roads.

[0012] S15. Collect thematic data of the entire city, including urban built-up area data and park entrance and exit data. The urban built-up area data adopts the common vector space data format, with the geometric features as surfaces and a scale of no less than 1:5000;

[0013] The park entrance and exit data adopts a common vector space data format, with geometric features as points and a scale of no less than 1:5000.

[0014] In the above method, the step S12 comprises the following steps:

[0015] S121. Collect building data across the city, using a common vector space data format, with geometric features as surfaces, a scale of no less than 1:5000, and including building area attributes;

[0016] S122. Collect data on the average number of mobile phone users staying in the city every day. The statistical period covers working days and holidays, and the total duration is not less than 7 days. The data format is universal raster space data, with a spatial resolution of not less than 1km and a scale of not less than 1:10000.

[0017] S123. Collect night light remote sensing satellite data for the entire city at a certain point in time within the past year. The data collection time is 20:00-23:00 Beijing time, the cloud coverage is less than 5%, and the common raster spatial data format is used. The spatial resolution is better than 500m and the scale is not less than 1:10000.

[0018] In the above method, in step S1, all collected data are preprocessed, including the following steps:

[0019] Create a 100m×100m evaluation grid covering the entire city;

[0020] Unify all collected vector data into the same vector space data format, and unify all collected raster data into the same raster space data format;

[0021] The spatial references of all collected spatial data were unified to the CGCS2000 plane projection coordinate system, using the Gauss Kruger 3° zone projection.

[0022] In the above method, step S2 comprises the following steps:

[0023] S21, respectively calculating the average number of people staying on holidays and weekdays for each evaluation grid, taking the maximum value of the two as the average number of people staying on a daily basis for the evaluation grid, and generating an average number of people staying on a daily basis index;

[0024] S22, converting the DN value of the night light remote sensing image into an absolute radiance value, resampling the radiance value to an evaluation grid, and generating a night light intensity index;

[0025] S23, counting the building area in each evaluation grid, calculating the ratio of the building area in each grid to the grid area as the building volume ratio, generating a building volume ratio index, and if the building spans across grids, dividing the building area according to the proportion of the building base area in different grids;

[0026] S24. Classify the roads according to their walkability, assigning walkability of 4, 3, 2, and 1 to greenways and park roads, branch roads, primary and secondary roads, and expressways, respectively, and establish a buffer zone based on the actual width attribute information of the road to form road surface data. Then, after vector-to-raster conversion, the road surface data is converted into raster data consistent with the evaluation grid, and the raster value is the walkability, thereby generating a walkability index;

[0027] S25. Dimensionless processing is performed on the daily average number of residents, night light intensity and building volume ratio, including the following processing formula:

[0028]

[0029] A hi , A′ hi are the index values ​​of the hth index of the i-th grid before and after dimensionless processing, A hk is the index value of the hth index of the kth grid before dimensionless processing, B is the scope of the built-up area, and n is the number of grids in the built-up area.

[0030] In the above method, step S3 comprises the following steps:

[0031] S31. Taking the evaluation grid within 300m of the ecological protection red line as the object, the human activity intensity level of each evaluation grid is calculated according to the average daily number of residents, building volume ratio and night light intensity index, and is divided into 1-4 levels;

[0032] S32. Taking the evaluation grid within 300m of the ecological protection red line as the object, calculate the ecological protection demand level of each evaluation grid according to the ecological function importance index and ecological environment sensitivity index, and divide it into 1-3 levels;

[0033] S33. Taking the evaluation grid within 300m of the ecological protection red line as the object, the walkability index value is used as the walkability classification of each evaluation grid, which is divided into 1-4 levels;

[0034] S34. Taking the evaluation grid within 100m of the ecological protection red line as the object, the priority of boundary stakes for each evaluation grid is calculated by integrating the human activity intensity grade and the ecological protection demand grade. Let the sum of the human activity intensity grade and the ecological protection demand grade be x, then:

[0035]

[0036] In addition, taking the evaluation grid within 300m of the ecological protection red line as the object, the human activity intensity grade and the walkability grade are comprehensively considered to calculate the priority of signboard placement for each evaluation grid. Assuming the sum of the human activity intensity grade and the walkability grade is y, then:

[0037]

[0038] In the above method, step S31 comprises the following steps:

[0039] S311. Select the evaluation grids within 300m of the ecological protection red line, and arrange them in descending order according to the building volume ratio index and the night light intensity index value. If any of the indicators ranks in the top 20%, it is classified as level 4 of human activity intensity;

[0040] S312. The remaining evaluation grids within 300m of the ecological protection red line, that is, the evaluation grids that are not classified as level 4 of human activity intensity, are divided into levels 1-3 based on the average daily number of visitors using the natural breakpoint method to obtain the final human activity intensity classification.

[0041] In the above method, step S32 comprises the following steps:

[0042] S321. Select the evaluation grid within 300m of the ecological protection red line, and superimpose the ecological function importance index and the ecological environment sensitivity index in an equal-weighted manner to form an ecological protection demand index;

[0043] S322. Use the natural breakpoint method to reclassify the ecological protection demand index to form an ecological protection demand grading result of 1-3 levels, with 3 being the highest and 1 being the lowest.

[0044] In the above method, in step S4, the boundary stake points are selected in combination with the boundary stake point layout priority and the ecological protection red line inflection point importance level, and morphological deviation correction is performed to obtain the boundary stake point recognition result, which includes the following steps:

[0045] S41, assigning the priority information of the boundary stakes based on the evaluation grid to the ecological protection red line, so as to form ecological protection red line sections classified by the priority of the boundary stakes;

[0046] S42. Modify the spatial continuity of the priority of boundary stake placement, and merge the ecological protection red line sections with a length of less than 100m into adjacent sections;

[0047] S43. Using the Point Remove algorithm of the ArcGIS Simplify Line tool, by setting different Simplification tolerance parameters, three simplified versions of the ecological protection red line data with a simplification degree from high to low were obtained, and the ArcGIS Feature To Point tool was used to extract the node coordinates of the three simplified versions of the ecological red line data, and the inflection point importance levels of 3, 2, and 1 were assigned respectively;

[0048] S44, comprehensively considering the inflection point importance level and the corrected boundary stake layout priority, screening the nodes, if the sum of the inflection point importance level and the boundary stake layout priority is greater than or equal to 4, retaining the node, otherwise deleting the node;

[0049] S45, morphological deviation marking, connect the filtered nodes in the original order to construct a polyline, then traverse the polyline in a clockwise or counterclockwise direction, and compare it with the original ecological protection red line data. When the deviation between the two exceeds a certain threshold, record the coordinates of the position with the largest deviation;

[0050] S46, local node addition, adding nodes at the position with the largest deviation, jumping to step S45 for deviation detection until all deviations are within the threshold range, and the final simplified ecological red line node is the boundary point identification result.

[0051] In the above method, in step S4, the candidate sign points are selected in combination with the sign point priority and the sign candidate point level, and the cluster point judgment is performed to obtain the sign point recognition result, which includes the following steps:

[0052] S401, extract three types of points within 300m of the ecological protection red line, namely, the park entrance and exit, the intersection of the pedestrian road and the ecological protection red line, and the pedestrian road intersection, as candidate sign points, and assign them level 3, level 2, and level 1 respectively;

[0053] S402, comprehensively considering the signboard placement priorities and the signboard candidate point levels, screening the signboard candidate points, if the sum of the signboard placement priorities and the signboard candidate point levels is greater than or equal to 4, retaining the candidate point, otherwise deleting the candidate point;

[0054] S403, detecting the distance between the candidate points obtained by the screening, and if the distance between any two candidate points is less than a distance threshold, marking the candidate points;

[0055] S404, deleting the marked candidate points in order of the signboard candidate point levels from low to high, until the distances between all candidate points are greater than the distance threshold, and the remaining candidate points are the signboard point selection results.

[0056] The beneficial effects of the present invention are as follows: it comprehensively considers factors such as the intensity of human activities, the degree of ecological protection demand, and the walkability, realizes the spatial quantitative evaluation of the priority of the layout of ecological protection red line boundary stakes and the priority of the layout of signboards, and combines the line simplification spatial analysis algorithm to realize the accurate identification of terrestrial ecological protection red line boundary stakes and signboards, reduces manual intervention, improves the efficiency of point identification, is suitable for solving layout plans under different point quantity restrictions, maximizes social benefits under cost constraints, and provides scientific and technological support for building a scientific and reasonable ecological protection red line identification system and achieving an effective balance between ecological protection effects and identification system construction costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Attached Figure 1 This is a flow chart of a dual-level selection method for terrestrial ecological protection red line identification points of the present invention. Figure 2 Schematic diagram of the process of obtaining boundary stake point identification results.

[0058] Attached Figure 3 A flow chart for obtaining the result of sign point recognition. DETAILED DESCRIPTION

[0059] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0060] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with the embodiments and drawings, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by technicians in this field without creative work are all within the scope of protection of the present invention. In addition, all the connection / connection relationships involved in the patent do not refer to the direct connection of components, but refer to the formation of a better connection structure by adding or reducing connection accessories according to the specific implementation situation. The various technical features in the invention can be combined interchangeably without conflicting with each other.

[0061] The location of boundary stakes and signboards is highly correlated with the intensity of human activities, which can be quickly identified using human activity perception big data, mainly including night light remote sensing data and mobile phone signaling data. Night light remote sensing data reflects the level of human economic activities by observing the intensity of nighttime lights on the surface. In recent years, the spatial resolution of night light remote sensing data has been continuously improved. The spatial resolution of my country's "Luojia-1" night light remote sensing satellite data is about 130 meters, which can support refined observations within the city. Mobile phone signaling data is the positioning data generated by mobile phone users during use. It is suitable for a wide range of human activity identification and analysis, and can effectively reflect the spatial distribution of human activity intensity.

[0062] Based on the problems faced by the point identification of the terrestrial ecological protection red line marking system and the current development status of human activity perception technology, the patent of this invention starts from the intensity of human activities and takes into account factors such as the ecological protection demand and pedestrian suitability to achieve spatial quantitative evaluation of the priority of ecological protection red line boundary stakes and signboards. Combined with the line simplification spatial analysis algorithm, a two-level selection method for the points of the terrestrial ecological protection red line marking system based on multi-source big data fusion is proposed. This method introduces multi-source big data such as human activities and ecological environment protection, and adopts a comprehensive evaluation and point identification double-layer selection method. It can solve the problems of excessive reliance on manual experience, lack of objective data support, high positioning arbitrariness, and low work efficiency in the traditional signboard point determination. It realizes the accurate identification of the terrestrial ecological protection red line boundary stakes and signboard points, reduces manual intervention, and improves the point identification efficiency. It is suitable for solving point distribution schemes under different point quantity restrictions and maximizes social benefits under cost constraints.

[0063] Reference Figure 1 As shown in FIG. 1 , a two-level selection method for terrestrial ecological protection red line identification points includes the following steps S1-S4:

[0064] S1. According to the specific geometric characteristics and scale requirements, collect the terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data, and preprocess all the collected data. The data preprocessing includes the following steps: make a 100m×100m evaluation grid covering the entire city; unify all the collected vector data into the same vector space data format, unify all the collected raster data into the same raster space data format; unify the spatial reference of all the collected spatial data into the CGCS2000 plane projection coordinate system, and use the Gauss Kruger 3° zoning projection. Through data preprocessing, all data can achieve unified data format and unified spatial reference.

[0065] Specifically, the collection of terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data according to specific geometric features and scale requirements includes the following steps:

[0066] S11. Collect the terrestrial ecological red line data for the point selection of the identification system, using a common vector space data format (such as Shapefile), with the geometric features as lines and a scale of no less than 1:10000. The terrestrial ecological red line data is the spatial data of the terrestrial ecological red line formed through the ecological red line delineation work, but has not yet been surveyed and marked for the ecological protection red line;

[0067] S12. Collect data on human activity intensity, including building data, daily average number of mobile phone users, and night light remote sensing satellite data;

[0068] Specifically, the step S12 includes the following steps:

[0069] S121. Collect building data across the city in a common vector spatial data format (e.g. Shapefile), with geometric features as surfaces, a scale of no less than 1:5000, and including building area attributes;

[0070] S122. Collect data on the average number of mobile phone users staying in the city every day. The data statistical period covers working days and holidays, and the total duration is not less than 7 days. The data is in a common raster spatial data format (such as GeoTIFF), with a spatial resolution of not less than 1 km and a scale of not less than 1:10000.

[0071] S123. Collect night light remote sensing satellite data of the entire city at a certain time point in the past year. The data collection time point is 20:00-23:00 Beijing time, the cloud coverage is less than 5%, and the general raster spatial data format (such as GeoTIFF) is used. The spatial resolution is better than 500m and the scale is not less than 1:10000;

[0072] S13. Collect ecological and environmental protection evaluation data for the entire city, including the ecological function importance and ecological and environmental sensitivity evaluation results data generated during the delineation of the ecological protection red line, using a common raster spatial data format (such as GeoTIFF), with a spatial resolution of no less than 200m and a scale of no less than 1:10000;

[0073] S14. Collect data on walkable roads throughout the city, including municipal roads, park roads, greenways and other types of roads for pedestrians. The data adopts a common vector space data format (such as Shapefile), with geometric features as lines and a scale of no less than 1:2000. All walkable roads include width attributes, and municipal roads include road type attributes. Road type attributes include expressways, primary and secondary roads (i.e., primary and secondary roads) and branch roads.

[0074] S15. Collect thematic data covering the entire city, including urban built-up area data and park entrance and exit data. The urban built-up area data shall adopt a common vector space data format (such as Shapefile), with geometric features as surfaces and a scale of no less than 1:5000; the park entrance and exit data shall adopt a common vector space data format (such as Shapefile), with geometric features as points and a scale of no less than 1:5000.

[0075] S2. Calculate the preprocessed data to obtain basic indicators, and perform normalization on the basic indicators;

[0076] Specifically, the step S2 includes the following steps:

[0077] S21, respectively calculating the average number of people staying on holidays and weekdays for each evaluation grid, taking the maximum value of the two as the average number of people staying on a daily basis for the evaluation grid, and generating an average number of people staying on a daily basis index;

[0078] S22, converting the DN value of the night light remote sensing image into an absolute radiance value, resampling the radiance value to an evaluation grid, and generating a night light intensity index;

[0079] S23, counting the building area in each evaluation grid, calculating the ratio of the building area in each grid to the grid area as the building volume ratio, generating a building volume ratio index, and if the building spans across grids, dividing the building area according to the proportion of the building base area in different grids;

[0080] S24. Classify the roads according to their walkability, assigning walkability of 4, 3, 2, and 1 to greenways and park roads, branch roads, primary and secondary roads, and expressways, respectively, and establish a buffer zone based on the actual width attribute information of the road to form road surface data. Then, after vector-to-raster conversion, the road surface data is converted into raster data consistent with the evaluation grid, and the raster value is the walkability, thereby generating a walkability index;

[0081] S25. The daily average number of people staying, night light intensity and building volume ratio are dimensionally processed to remove the impact of different dimensions. The dimensionless processing is achieved by the following formula:

[0082]

[0083] A hi , A′ hi are the index values ​​of the hth index of the i-th grid before and after dimensionless processing, A hk is the index value of the hth index of the kth grid before dimensionless processing, B is the scope of the built-up area, and n is the number of grids in the built-up area.

[0084] S3. Calculate the human activity intensity classification, ecological protection demand classification, and walkability classification based on the basic indicators, and then calculate the priority of boundary stakes based on the human activity intensity classification and ecological protection demand classification, and calculate the priority of signboards based on the human activity intensity classification and walkability classification;

[0085] Specifically, the step S3 includes the following steps:

[0086] S31. Taking the evaluation grid within 300m of the ecological protection red line as the object, the human activity intensity level of each evaluation grid is calculated according to the average daily number of residents, building volume ratio and night light intensity index, and is divided into 1-4 levels;

[0087] Specifically, the step S31 includes the following steps:

[0088] S311. Select the evaluation grids within 300m of the ecological protection red line, and arrange them in descending order according to the building volume ratio index and the night light intensity index value. If any of the indicators ranks in the top 20%, it is classified as level 4 of human activity intensity;

[0089] S312. The remaining evaluation grids within 300m of the ecological protection red line, i.e., the evaluation grids not classified as level 4 of human activity intensity, are divided into levels 1-3 according to the average daily number of residents using the Nature breaks method to obtain the final human activity intensity classification;

[0090] S32. Taking the evaluation grid within 300m of the ecological protection red line as the object, calculate the ecological protection demand level of each evaluation grid according to the ecological function importance index and ecological environment sensitivity index, and divide it into 1-3 levels;

[0091] Specifically, the step S32 includes the following steps:

[0092] S321. Select the evaluation grid within 300m of the ecological protection red line, and superimpose the ecological function importance index and the ecological environment sensitivity index in an equal-weighted manner to form an ecological protection demand index;

[0093] S322. Reclassify the ecological protection demand index using the natural breakpoint method to form an ecological protection demand classification, which includes 1-3 levels, with 3 being the highest and 1 being the lowest;

[0094] S33. Taking the evaluation grid within 300m of the ecological protection red line as the object, the walkability index value is used as the walkability classification of each evaluation grid, which is divided into 1-4 levels;

[0095] S34. Taking the evaluation grid within 100m of the ecological protection red line as the object, the priority of boundary stakes for each evaluation grid is calculated by integrating the human activity intensity grade and the ecological protection demand grade. Let the sum of the human activity intensity grade and the ecological protection demand grade be x, then:

[0096]

[0097] In addition, taking the evaluation grid within 300m of the ecological protection red line as the object, the human activity intensity grade and the walkability grade are comprehensively considered to calculate the priority of signboard placement for each evaluation grid. Assuming the sum of the human activity intensity grade and the walkability grade is y, then:

[0098]

[0099] S4. Select boundary stake points based on the boundary stake point priority and the ecological protection red line turning point importance level, and perform morphological deviation correction to obtain boundary stake point recognition results. In addition, select signboard candidate points based on the signboard point priority and the signboard candidate point level, and perform cluster point judgment to obtain signboard point recognition results.

[0100] Specifically, refer to Figure 2 As shown, the method of selecting boundary stake points in combination with the boundary stake point layout priority and the importance level of the ecological protection red line inflection point, and carrying out morphological deviation correction to obtain boundary stake point recognition results includes the following steps:

[0101] S41. Use the spatial overlay analysis method to assign the priority information of boundary stakes based on the evaluation grid to the ecological protection red line, and form ecological protection red line sections classified by boundary stakes priority;

[0102] S42. Modify the spatial continuity of the priority of boundary stake placement, and merge the ecological protection red line sections with a length of less than 100m into adjacent sections;

[0103] S43. Using the PointRemove algorithm (i.e., the node removal algorithm) of the ArcGIS Simplify Line tool (i.e., the line simplification tool of ArcGIS software), by setting different Simplification tolerance (i.e., simplification tolerance) parameters (the recommended parameters are 500m, 200m, and 50m), three simplified versions of the ecological protection red line data with simplification degrees from high to low are obtained, and using the ArcGIS Feature To Point tool (i.e., ArcGIS feature to point) tool to respectively extract the node coordinates of the three simplified versions of the ecological red line data, and assign inflection point importance levels of level 3, level 2, and level 1, respectively, which are the inflection point importance levels of the ecological protection red line;

[0104] S44, comprehensively considering the inflection point importance level in step S43 and the boundary stake layout priority level corrected in step S42, screening the nodes in step S43, if the sum of the inflection point importance level and the boundary stake layout priority level is greater than or equal to 4, retaining the node, otherwise deleting the node;

[0105] S45, morphological deviation marking, connecting the nodes screened in step S44 in the original order to construct a polyline, then traversing the polyline in a clockwise or counterclockwise direction, and comparing it with the original ecological protection red line data. When the deviation between the two exceeds a certain threshold (recommended 200m), record the coordinates of the position with the largest deviation;

[0106] S46, local node addition, adding nodes at the position with the largest deviation in step S45, jumping to step S45, and performing deviation detection again until all deviations are within the threshold range, and the simplified ecological red line node finally obtained is the boundary point identification result.

[0107] Further, see Figure 3 As shown, the method of selecting candidate sign points by combining the sign point priority and the sign candidate point level, and performing cluster point judgment to obtain the sign point recognition result includes the following steps:

[0108] S401, extract three types of points within 300m of the ecological protection red line, namely, the park entrance and exit, the intersection of the pedestrian road and the ecological protection red line, and the pedestrian road intersection, as candidate points for signboards, and assign them the levels of 3, 2, and 1 respectively;

[0109] S402, comprehensively considering the signboard placement priorities and the signboard candidate point levels, screening the signboard candidate points, if the sum of the signboard placement priorities and the signboard candidate point levels is greater than or equal to 4, retaining the candidate point, otherwise deleting the candidate point;

[0110] S403, detecting the distance between the candidate points screened in step S402, and if the distance between any two candidate points is less than a distance threshold (recommended to be 50m), marking the candidate points;

[0111] S404, deleting the marked candidate points in order of the signboard candidate point levels from low to high, until the distances between all candidate points are greater than the distance threshold, and the remaining candidate points are the signboard point selection results.

[0112] The patent of this invention comprehensively considers factors such as the intensity of human activities, the demand for ecological protection, and the walkability, and realizes the spatial quantitative evaluation of the priority of the layout of ecological protection red line boundary stakes and signboards. Combined with the line simplification spatial analysis algorithm, it realizes the accurate identification of terrestrial ecological protection red line boundary stakes and signboards, reduces human intervention, and improves the efficiency of point identification. It is suitable for solving layout plans under different point quantity restrictions, maximizes social benefits under cost constraints, and provides scientific and technological support for building a scientific and reasonable ecological protection red line identification system and achieving an effective balance between ecological protection effects and identification system construction costs.

[0113] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A two-level selection method for terrestrial ecological protection red line identification points. Features: The steps include: S1. Collect terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data according to specific geometric characteristics and scale requirements, and pre-process all collected data; S2. Calculate the preprocessed data to obtain basic indicators, and perform normalization on the basic indicators; S3. Calculate the human activity intensity classification, ecological protection demand classification, and walkability classification based on the basic indicators, and then calculate the priority of boundary stakes based on the human activity intensity classification and ecological protection demand classification, and calculate the priority of signboards based on the human activity intensity classification and walkability classification; S4. Select boundary stake points based on the boundary stake point priority and the ecological protection red line turning point importance level, and perform morphological deviation correction to obtain boundary stake point recognition results. In addition, select signboard candidate points based on the signboard point priority and the signboard candidate point level, and perform cluster point judgment to obtain signboard point recognition results. The step S4 includes the following steps: S44, comprehensively considering the inflection point importance level and the corrected boundary stake layout priority, screening the nodes, if the sum of the inflection point importance level and the boundary stake layout priority is greater than or equal to 4, retaining the node, otherwise deleting the node; S45, morphological deviation marking, connect the filtered nodes in the original order to construct a polyline, then traverse the polyline in a clockwise or counterclockwise direction, and compare it with the original ecological protection red line data. When the deviation between the two exceeds a certain threshold, record the coordinates of the position with the largest deviation; S46, local node addition, adding nodes at the position with the largest deviation, jumping to step S45 for deviation detection, until all deviations are within the threshold range, and the simplified ecological red line node finally obtained is the boundary point identification result; S402, comprehensively considering the signboard placement priorities and the signboard candidate point levels, screening the signboard candidate points, if the sum of the signboard placement priorities and the signboard candidate point levels is greater than or equal to 4, retaining the candidate point, otherwise deleting the candidate point; S403, detecting the distance between the candidate points obtained by the screening, and if the distance between any two candidate points is less than a distance threshold, marking the candidate points; S404, deleting the marked candidate points in order of the signboard candidate point levels from low to high, until the distances between all candidate points are greater than the distance threshold, and the remaining candidate points are the signboard point selection results.

2. A dual-level selection method for terrestrial ecological protection red line identification points as described in claim 1, Features: In the step S1, according to specific geometric features and scale requirements, the terrestrial ecological protection red line data, human activity intensity data, ecological environment protection evaluation data, walkable road data and thematic data are collected, including the following steps: S11. Collect the terrestrial ecological redline data for point selection, using a common vector space data format, with the geometric features being lines and a scale of no less than 1:10000; S12. Collect data on human activity intensity, including building data, daily average number of mobile phone users, and night light remote sensing satellite data; S13. Collect ecological and environmental protection evaluation data for the entire city, including the ecological function importance and ecological and environmental sensitivity evaluation results data generated during the delineation of the ecological protection red line, using a common raster spatial data format with a spatial resolution of no less than 200 m and a scale of no less than 1:10000; S14. Collect the data of walkable roads in the whole city. Walkable roads include municipal roads, park roads and greenways. The data adopts the universal vector space data format. The geometric features are lines. The scale is not less than 1:2000. All walkable roads include width attributes. Municipal roads include road type attributes. Road type attributes include expressways, primary and secondary roads and branch roads. S15. Collect thematic data of the entire city, including urban built-up area data and park entrance and exit data. The urban built-up area data adopts the common vector space data format, with the geometric features as surfaces and a scale of no less than 1:5000; The park entrance and exit data adopts a common vector space data format, with geometric features as points and a scale of no less than 1:5000.

3. A dual-level selection method for terrestrial ecological protection red line identification points as described in claim 2, Features: The step S12 comprises the following steps: S121. Collect building data across the city, using a common vector space data format, with geometric features as surfaces, a scale of no less than 1:5000, and including building area attributes; S122. Collect data on the average number of mobile phone users staying in the city every day. The statistical period covers working days and holidays, and the total duration is not less than 7 days. The data format is universal raster space data, with a spatial resolution of not less than 1 km and a scale of not less than 1:10000. S123. Collect night light remote sensing satellite data for the entire city at a certain point in time within the past year. The data collection time is 20:00-23:00 Beijing time, the cloud coverage is less than 5%, and the common raster spatial data format is used. The spatial resolution is better than 500 m and the scale is not less than 1:10000.

4. A dual-level selection method for terrestrial ecological protection red line identification points as claimed in claim 3, Features: In step S1, all collected data are preprocessed, including the following steps: Create a 100m×100m evaluation grid covering the entire city; Unify all collected vector data into the same vector space data format, and unify all collected raster data into the same raster space data format; The spatial references of all collected spatial data were unified to the CGCS2000 plane projection coordinate system, using the Gauss Kruger 3° zone projection.

5. A dual-level selection method for terrestrial ecological protection red line identification points as claimed in claim 4, Features: The step S2 comprises the following steps: S21, respectively calculating the average number of people staying on holidays and weekdays for each evaluation grid, taking the maximum value of the two as the average number of people staying on a daily basis for the evaluation grid, and generating an average number of people staying on a daily basis index; S22, converting the DN value of the night light remote sensing image into an absolute radiance value, resampling the radiance value to an evaluation grid, and generating a night light intensity index; S23, counting the building area in each evaluation grid, calculating the ratio of the building area in each grid to the grid area as the building volume ratio, generating a building volume ratio index, and if the building spans across grids, dividing the building area according to the proportion of the building base area in different grids; S24. Classify the roads according to their walkability, assigning walkability of 4, 3, 2, and 1 to greenways and park roads, branch roads, primary and secondary roads, and expressways, respectively, and establish a buffer zone based on the actual width attribute information of the road to form road surface data. Then, after vector-to-raster conversion, the road surface data is converted into raster data consistent with the evaluation grid, and the raster value is the walkability, thereby generating a walkability index; S25. Dimensionless processing is performed on the daily average number of residents, night light intensity and building volume ratio, including the following processing formula: , Respectively i The grid h The index values ​​before and after dimensionless processing, is the index value of the hth index of the kth grid before dimensionless processing, B The built-up area is n is the number of grids in the built-up area.

6. A dual-level selection method for terrestrial ecological protection red line identification points as claimed in claim 5, Features: The step S3 comprises the following steps: S31. Taking the evaluation grid within 300m of the ecological protection red line as the object, the human activity intensity level of each evaluation grid is calculated according to the average daily number of residents, building volume ratio and night light intensity index, and is divided into 1-4 levels; S32. Taking the evaluation grid within 300m of the ecological protection red line as the object, calculate the ecological protection demand level of each evaluation grid according to the ecological function importance index and ecological environment sensitivity index, and divide it into 1-3 levels; S33. Taking the evaluation grid within 300m of the ecological protection red line as the object, the walkability index value is used as the walkability classification of each evaluation grid, which is divided into 1-4 levels; S34. Taking the evaluation grid within 100 m of the ecological protection red line as the object, comprehensively grading the intensity of human activities and the degree of ecological protection needs, calculate the priority of boundary stake layout for each evaluation grid. Let the sum of the grading of the intensity of human activities and the grading of the degree of ecological protection needs be x , then: ； In addition, taking the evaluation grid within 300m of the ecological protection red line as the object, the human activity intensity grade and the walkability grade are integrated to calculate the priority of signboard placement for each evaluation grid. The sum of the human activity intensity grade and the walkability grade is set as y ,but: 。 7. A dual-level selection method for terrestrial ecological protection red line identification points as claimed in claim 6, Features: The step S31 includes the following steps: S311. Select the evaluation grids within 300m of the ecological protection red line, and arrange them in descending order according to the building volume ratio index and the night light intensity index value. If any of the indicators ranks in the top 20%, it is classified as level 4 of human activity intensity; S312. The remaining evaluation grids within 300m of the ecological protection red line, that is, the evaluation grids that are not classified as level 4 of human activity intensity, are divided into levels 1-3 based on the average daily number of visitors using the natural breakpoint method to obtain the final human activity intensity classification.

8. A dual-level selection method for terrestrial ecological protection red line identification points as claimed in claim 7, Features: The step S32 comprises the following steps: S321. Select the evaluation grids within 300 m of the ecological protection red line, and superimpose the ecological function importance index and the ecological environment sensitivity index in an equal-weight manner to form the ecological protection demand degree index. S322. Use the natural breakpoint method to reclassify the ecological protection demand degree index to form the ecological protection demand degree classification results of levels 1-3, with level 3 being the highest and level 1 being the lowest.

9. A two-level selection method for the identification points of the land ecological protection red line as described in claim 8, characterized in that: In step S4, the boundary marker points are selected by combining the boundary marker layout priority and the importance level of the ecological protection red line inflection points, and the morphological deviation correction is carried out to obtain the boundary marker point recognition result, including the following steps: S41. Assign the boundary marker layout priority information based on the evaluation grid to the ecological protection red line to form ecological protection red line sections classified by the boundary marker layout priority. S42. Correct the spatial continuity of the boundary marker layout priority, and merge the ecological protection red line sections with a length less than 100 m into the adjacent sections. S43. Use the Point Remove algorithm of the ArcGIS Simplify Line tool, and by setting different Simplification tolerance parameters, obtain three ecological protection red line simplified version data with decreasing simplification degrees, and use the ArcGIS Feature To Point tool to extract the node coordinates of these three ecological protection red line simplified version data respectively, and assign the inflection point importance levels of level 3, level 2, and level 1 respectively.

10. A two-level selection method for the identification points of the land ecological protection red line as described in claim 8, characterized in that: In step S4, the sign candidate points are selected by combining the sign layout priority and the sign candidate point level, and the aggregation point judgment is carried out to obtain the sign point recognition result, including the following steps: S401. Extract three types of points within 300 m of the ecological protection red line, namely the park entrances and exits, the intersections of the walking paths and the ecological protection red line, and the intersections of the walking paths, as the sign candidate points, and assign the sign candidate point levels of level 3, level 2, and level 1 respectively.

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