Method for identifying important ecological sources of urban green space ecological network

Abstract

The application relates to a kind of urban green space ecological network important ecological source place identification method, comprising: 1, determining the service object and dominant function of the object city ecological source place;2, screening out candidate ecological source place;3, constructing the index system of urban ecological source place identification based on social-ecological complex function, obtaining the classification grid map of each index corresponding to candidate ecological source place;4, the classification grid map data of each index is input into spatial principal component analysis tool to obtain the weight value of each index;5, the classification grid of all indexes is spatially superimposed, and the SEI is obtained by weighted calculation, and the importance classification of SEI is carried out;6, according to the preset importance level threshold, the corresponding grid layer is extracted and converted into vector surface layer, and the important ecological source place is extracted from the vector surface layer of candidate ecological source place.The application considers the social function and ecological function of ecological source place, meets the demand of human habitation and the demand of habitat survival of focal species, and realizes the identification of important ecological source place.

Description

Technical Field

[0001] This invention relates to the field of urban ecological planning and construction technology, specifically to a method for identifying important ecological sources in urban ecological networks. Background Technology

[0002] Urban ecological source areas refer to the existing habitats of native species in cities, as well as the origin points for their dissemination and maintenance. They are the core components of the urban green space ecological network and an important foundation for constructing an urban ecological security pattern. Accurately identifying ecological source areas in highly urbanized areas and then connecting them into a stable network system through ecological corridors plays a vital role in restoring and maintaining biological connectivity, improving the urban ecological environment, protecting the health of urban residents, and reshaping the harmonious relationship between humans and nature.

[0003] Urban green spaces are the primary form of urban ecological sources. Currently, the identification of urban ecological sources often involves directly selecting large green space patches with good habitat quality (such as scenic areas and forest parks), or conducting comprehensive assessments and selections based on ecosystem service functions and ecological sensitivity. However, these source extraction methods primarily emphasize the ecosystem service functions of urban green spaces, paying less attention to the social service functions they provide, such as landscape, culture, and recreation. Furthermore, they lack sufficient consideration of the role of each green space patch within the overall landscape pattern and its relationship with the surrounding environment.

[0004] The identification of ecological source areas in highly urbanized areas should adopt a paradigm based on socio-ecological functions. This paradigm should emphasize the ecological service functions of urban green spaces while also considering their impact on the surrounding living environment and socio-cultural processes. Therefore, there is an urgent need for a method to identify important ecological source areas in urban green space ecological networks. This method should accurately identify important ecological source areas in highly urbanized areas from the perspective of socio-ecological functions, laying the foundation for constructing a functionally integrated and structurally sound urban green space ecological network. Summary of the Invention

[0005] To achieve the above-mentioned technical objectives, this invention provides a method for identifying important ecological sources in urban green space ecological networks. This method takes into account both the social and ecological functions of ecological sources, meets the needs of human habitation and the habitat and survival needs of key species, and achieves the identification of important ecological sources in a more scientific and objective manner.

[0006] The technical objective of this invention is achieved through the following technical solution:

[0007] A method for identifying important ecological source areas in urban green space ecological networks, the method comprising the following steps:

[0008] Step 1: Determine the service targets and dominant functions of the target city's ecological source area;

[0009] Step 2: Select candidate ecological source areas from the urban green space vector map patches;

[0010] Step 3: Construct an indicator system for identifying urban ecological source areas based on social-ecological composite functions, and obtain a hierarchical raster map of each indicator corresponding to the candidate ecological source areas within the indicator system;

[0011] Step 4: Input the hierarchical raster data of each indicator into the spatial principal component analysis tool to obtain the weight value of each indicator;

[0012] Step 5: Based on the GIS platform, spatially overlay the hierarchical raster maps of each indicator according to their weight values, calculate the social-ecological function index (SEI) of each raster cell in the candidate ecological source area using weighted average, and classify the importance of the SEI.

[0013] Step 6: In the GIS platform, filter the corresponding hierarchical raster map according to the preset Social-Ecological Function Index (SEI) threshold and convert it into a vector polygon layer; use the layer selection tool by location to extract the vector polygon layer from the vector polygon layer of the candidate ecological source area, and save the extracted vector polygon layer as a separate vector layer to complete the extraction of important ecological source areas.

[0014] Furthermore, in step 3, the indicator system includes social function evaluation indicators and ecological function evaluation indicators; the social function evaluation indicators include green space service coverage radius, green space service facilities, actual green space usage level, and green space accessibility; the ecological function evaluation indicators include green space habitat quality, green space vegetation quality, green space connectivity importance, and green space mitigation of the heat island effect.

[0015] Furthermore, in step 3, the social function evaluation indicators and ecological function indicators are divided into several levels and a value is assigned to each level to obtain a hierarchical raster map corresponding to each indicator in the indicator system.

[0016] Further, in step 4, in the GIS platform, the hierarchical raster map corresponding to each indicator is input into the Principal Component tool for spatial principal component analysis to obtain the loading, eigenvalue and cumulative contribution rate of each principal component; the principal components whose cumulative contribution rate meets the threshold condition are selected according to the set threshold of the cumulative contribution rate, and the weight of each indicator is calculated in sequence according to the loading, eigenvalue and cumulative contribution rate of the indicators under the selected principal components.

[0017] Furthermore, in step 5, when calculating the Social-Ecological Function Index (SEI):

[0018]

[0019] Wherein, SEI is the socio-ecological function index of the i-th raster pixel in the candidate ecological source area; P ij W represents the j-th indicator of the i-th raster pixel in the candidate ecological source area. j The weights of each indicator.

[0020] Furthermore, in step 2, when screening candidate ecological source areas, spatial distribution data of existing green space patches in the target city is obtained. In GIS software, the aggregation surface tool is used to classify and aggregate the green space patches within a set range, and the aggregated green space patches are screened according to a set area threshold to obtain candidate ecological source areas.

[0021] Furthermore, green space patches include park green space, protective green space, ancillary green space, regional green space, and plaza land.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] 1. When identifying important ecological sources using the method of this invention, the social and ecological attributes of urban green spaces are fully considered, taking into account both human ecological livability needs and the habitat and survival needs of representative focal species, and an urban ecological source identification index system based on social-ecological composite functions is constructed.

[0024] 2. In the urban ecological source identification index system based on social-ecological composite functions, not only are the characteristics and vertical processes of urban green space patches, such as area size, vegetation quality, and cooling effect, considered, but also indicators that can reflect their importance to the overall structure and function of the green space ecological network in the horizontal dimension, such as habitat quality, connectivity importance, and spatial accessibility, are also included.

[0025] 3. In determining the weights of the indicators, based on the hierarchical raster map of each indicator, the SPCA method is used to perform linear transformation on the raster data to reduce the dimensionality, quantitatively reflecting the impact of different indicators on the importance of the ecological source area. This avoids the problem of strong subjectivity when determining the weights of each indicator based on AHP or expert experience in the traditional way, and determines the weights of each indicator more objectively. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method for identifying important ecological sources in the urban green space ecological network according to the present invention.

[0027] Figure 2 This is a schematic diagram showing the distribution of the candidate ecological source areas selected in this invention.

[0028] Figure 3 This is a graded raster chart of the importance of green space connectivity, one of the eight indicators in this invention.

[0029] Figure 4 This is a hierarchy map of the importance of candidate ecological source areas in this invention.

[0030] Figure 5 This is a distribution map of important ecological source areas identified according to the method of the present invention in this embodiment of the invention. Detailed Implementation

[0031] The technical solution of the present invention will be further described below with reference to specific embodiments:

[0032] A method for identifying important ecological source areas in urban green space ecological networks, taking Minhang District of Shanghai as an example, such as... Figure 1 As shown, it includes the following steps:

[0033] Step 1: Determine the service targets and dominant functions of the ecological source areas of the target city. The service targets include urban residents and representative key protected animals of the target city. The identification of important ecological source areas should take into account both the functional needs of human ecological livability and the habitat and survival of key species.

[0034] The primary target population for the ecological source area in Minhang District will be urban residents living and working in the district, as well as small mammals (weasels, hedgehogs) and amphibians and reptiles (frogs, sauropods) commonly found in the district. The selection of the urban ecological source area takes into account both human habitability and the habitat and survival needs of the key species.

[0035] Step 2: Using existing natural and artificial vegetation (urban green space) in the city as the identification object of important ecological sources in the city, obtain the spatial distribution data of the existing green space patches in the target city. Green space patches include park green space, protective green space, ancillary green space, regional green space and square land.

[0036] In GIS software, the aggregation surface tool is used to classify and aggregate existing green space patches within a certain distance range, and then perform preliminary screening of the aggregated green space patches according to a set area threshold as candidate ecological source areas.

[0037] Technicians collected and prepared vector distribution data of urban green spaces in Minhang District, Shanghai. Using GIS software, they employed the aggregation surface tool to classify and aggregate existing green space patches with Euclidean distances within 10 meters. These aggregated green space patches were then screened based on an area threshold of 0.05 square kilometers as potential ecological source areas. A total of 454 potential ecological sources were obtained, covering a total area of ​​52.25 square kilometers, accounting for 14.02% of the total area of ​​Minhang District. Figure 2 As shown. Among them, there are 85 candidate ecological source sites with the attribute of park green space, with a total area of ​​11.57 square kilometers; 46 candidate ecological source sites with the attribute of protective green space, with a total area of ​​5.17 square kilometers; 237 candidate ecological source sites with the attribute of protective green space, with a total area of ​​24.78 square kilometers; and 86 candidate ecological source sites with the attribute of regional green space, with a total area of ​​10.23 square kilometers.

[0038] Step 3: Construct an urban ecological source area identification index system based on social-ecological composite functions. The index system includes social function evaluation indicators and ecological function evaluation indicators. Social function evaluation indicators include green space service coverage radius, green space service facilities, actual green space use level and green space accessibility. Ecological function evaluation indicators include green space habitat quality, green space vegetation quality, green space connectivity importance and green space mitigation of heat island effect.

[0039] Using the calculation and assignment methods shown in Table 1, a hierarchical grid map corresponding to the eight indicators of the target city was obtained. Taking the importance of green space connectivity as an example, for instance... Figure 3 As shown.

[0040]

[0041]

[0042] Step 4: Based on the hierarchical raster plots of the above 8 indicators, spatial principal component analysis (SPCA) is used to determine the weight of each indicator.

[0043] SPCA is based on statistical principles and GIS. It maps each spatial variable to a matrix and assigns the degree of influence of related spatial variables on the dependent variable to the corresponding principal component factors. It can also clearly implement the principal component factor analysis results to each grid cell corresponding to the space, making the original principal component analysis results intuitively extended to two-dimensional space.

[0044] In the GIS platform, the raster map corresponding to each indicator is input into the Principal Component Analysis tool to obtain the loading, eigenvalue and cumulative contribution rate of each principal component, as shown in Table 3.

[0045] In this embodiment, the cumulative contribution rate of the fourth principal component reaches 88.42%, exceeding the preset threshold of 85%. The linear combination coefficients of each indicator (as shown in Table 4), the comprehensive score table (as shown in Table 5), and the weight values ​​of each indicator (as shown in Table 5) are calculated sequentially using the load, eigenvalue, and cumulative contribution rate of the fourth principal component.

[0046] The specific calculation process for the weights of each indicator is as follows:

[0047] (1) Preprocess each raster data: normalize and standardize the original data;

[0048] (2) Determine the coefficients of the principal components in each linear combination: coefficient = loading / square root of the corresponding eigenvalue;

[0049] (3) Determine the comprehensive coefficient of each factor in the comprehensive score model: Comprehensive coefficient = (coefficient (component 1) * corresponding contribution rate + coefficient (component 2) * corresponding contribution rate + ... coefficient (component N) * corresponding contribution rate (component N)) / cumulative contribution rate;

[0050] (4) Determine the weight of each factor: Normalize the coefficients of each factor in the comprehensive scoring model, W j =Comprehensive coefficient N / Sum of comprehensive coefficients of all factors.

[0051] Table 2 Principal Component Loadings

[0052] index First principal component Second principal component Third principal component Fourth principal component Green space service coverage radius 0.25703 0.53483 -0.16935 0.16276 Green space service facilities level -0.23657 0.46488 0.17498 -0.8175 Cooling effect of green spaces 0.41178 -0.08312 0.15201 0.00958 Green space accessibility -0.37021 0.23614 0.35651 0.41073 Importance of Green Space Connectivity 0.21635 0.45 -0.17114 0.10545 Green space habitat quality 0.53099 -0.09997 0.72701 -0.11777 Actual utilization level of green space -0.36719 0.2317 0.47892 0.28478 Green space vegetation quality 0.32829 0.4111 -0.0574 0.17413

[0053] Table 3. Principal Component Characteristics and Contribution Rates

[0054] principal component Eigenvalues Contribution rate / % Cumulative contribution rate / % First principal component 0.95891 43.7033 43.7 Second principal component 0.53293 24.2889 67.99 Third principal component 0.28527 13.0017 80.99 Fourth principal component 0.1631 7.4337 88.42

[0055] Table 4 Linear combination coefficients

[0056] index First principal component Second principal component Third principal component Fourth principal component Green space service coverage radius 0.262479 0.732623 -0.31707 0.403015 Green space service facilities level -0.24159 0.636804 0.327612 -2.02423 Cooling effect of green spaces 0.42051 -0.11386 0.284606 0.023721 Green space accessibility -0.37806 0.32347 0.667488 1.01702 Importance of Green Space Connectivity 0.220937 0.616421 -0.32042 0.261108 Green space habitat quality 0.542247 -0.13694 1.36117 -0.29161 Actual utilization level of green space -0.37497 0.317388 0.896675 0.705152 Green space vegetation quality 0.33525 0.563135 -0.10747 0.431168

[0057] Table 5 Model Score Coefficients and Weight Results

[0058] index Overall score coefficient Weight Green space service coverage radius 0.418245197 0.171 Green space service facilities level 0.033512279 0.014 Cooling effect of green spaces 0.320412174 0.131 Green space accessibility 0.185648385 0.076 Importance of Green Space Connectivity 0.353368104 0.144 Green space habitat quality 0.506034808 0.206 Actual utilization level of green space 0.192983085 0.079 Green space vegetation quality 0.440843108 0.180

[0059] Step 5: Based on the GIS platform, using the raster calculator tool, according to the weight values ​​of each indicator calculated in Step 4, the hierarchical raster maps of each indicator obtained in Step 3 are spatially overlaid to calculate the social-ecological function index (SEI) of each raster cell in all candidate ecological source areas, and the SEI is classified by importance.

[0060] When calculating the Social-Ecological Function Index (SEI):

[0061]

[0062] Wherein, SEI is the socio-ecological function index of the i-th raster pixel in the candidate ecological source area; P ij W represents the j-th indicator of the i-th raster pixel in the candidate ecological source area. j The weights of each indicator.

[0063] In the GIS platform, a reclassification tool was used to reclassify the SEI calculation results of each raster cell in all candidate ecological source areas of Minhang District into 5 levels according to the natural breakpoint method: extremely important, important, moderate, unimportant, and extremely unimportant. The number of extremely important and important raster cells accounted for only 16.2% and 16.1% of the total, respectively. Figure 4 As shown.

[0064] Step 6: In the GIS platform, first use the attribute extraction tool to extract the corresponding raster pixels according to the preset level threshold (in this embodiment, two levels, extremely important and important, are selected). Then, use the raster to polygon tool to convert the raster map containing these two levels of raster pixels into a vector polygon layer. Next, use the location to select the layer tool to extract the identified important ecological source patches in the vector polygon layer of the candidate ecological source areas through the intersection relationship, and save them as separate vector layers. Finally, the extraction of important ecological source areas is completed.

[0065] In this embodiment, a total of 146 important ecological source areas were extracted in Minhang District, Shanghai, covering a total area of ​​19.26 square kilometers, accounting for 36.9% of all candidate ecological source areas and 5.2% of the total area of ​​Minhang District. Among them, 43 important ecological source areas are classified as park green spaces, covering a total area of ​​6.7 square kilometers; 10 important ecological source areas are classified as protective green spaces, covering a total area of ​​1.25 square kilometers; 24 important ecological source areas are classified as auxiliary green spaces, covering a total area of ​​3.76 square kilometers; and 69 important ecological source areas are classified as regional green spaces, covering a total area of ​​7.55 square kilometers. Typical important ecological source areas include Minhang Cultural Park in Qibao Town, Minhang Sports Park and Meiyuan Garden in Xinzhuang Town, Pujiang First Bay Park in Wujing Town, and Pujiang Country Park in Pujiang Town, etc. Figure 5 As shown.

[0066] This embodiment is merely a further explanation of the present invention and is not intended to limit the present invention. Those skilled in the art can make non-inventive modifications to this embodiment as needed after reading this specification, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A method for identifying important ecological source areas in urban green space ecological networks, characterized in that, The method includes the following steps: Step 1: Determine the service targets and dominant functions of the target city's ecological source area; Step 2: Select candidate ecological source areas from the urban green space vector map patches; Step 3: Construct an urban ecological source area identification index system based on social-ecological composite functions, and obtain a hierarchical raster map of each indicator corresponding to the candidate ecological source areas within the index system. The index system includes social function evaluation indicators and ecological function evaluation indicators. The social function evaluation indicators include green space service coverage radius, green space service facilities, actual green space usage level, and green space accessibility. The ecological function evaluation indicators include green space habitat quality, green space vegetation quality, green space connectivity importance, and green space mitigation of the urban heat island effect. Step 4: Input the hierarchical raster data of each indicator into the spatial principal component analysis tool to obtain the weight value of each indicator; Step 5: Based on the GIS platform, spatially overlay the hierarchical raster maps of each indicator according to their weight values, calculate the social-ecological function index (SEI) of each raster cell in the candidate ecological source area using weighted average, and classify the social-ecological function index (SEI). Step 6: In the GIS platform, filter the corresponding hierarchical raster map according to the preset Social-Ecological Function Index (SEI) threshold and convert it into a vector polygon layer; use the layer selection tool by location to extract the vector polygon layer from the vector polygon layer of the candidate ecological source area, and save the extracted vector polygon layer as a separate vector layer to complete the extraction of important ecological source areas.

2. The method for identifying important ecological source areas of urban green space ecological networks according to claim 1, characterized in that, The social function evaluation indicators and ecological function indicators are divided into several levels and each level is assigned a value, resulting in a hierarchical raster map of each indicator corresponding to the candidate ecological source areas within the indicator system.

3. The method for identifying important ecological source areas of urban green space ecological networks according to claim 1, characterized in that, In step 4, in the GIS platform, the hierarchical raster map corresponding to each indicator is input into the PrincipalComponent tool for spatial principal component analysis to obtain the loading, eigenvalue and cumulative contribution rate of each principal component; the principal components whose cumulative contribution rate meets the threshold condition are selected according to the set threshold of the cumulative contribution rate, and the weight of each indicator is calculated in sequence according to the loading, eigenvalue and cumulative contribution rate of the indicators under the selected principal components.

4. The method for identifying important ecological source areas of urban green space ecological networks according to claim 1, characterized in that, When calculating the Social-Ecological Function Index (SEI): ， Among them, SEI is the socio-ecological function index of the i-th raster pixel of the candidate ecological source area; The j-th indicator for the i-th raster pixel in the candidate ecological source area; The weights of each indicator.

5. The method for identifying important ecological source areas of urban green space ecological networks according to claim 1, characterized in that, In step 2, when screening candidate ecological source areas, spatial vector data of existing green space patches in the target city are obtained. In GIS software, the aggregation surface tool is used to classify and aggregate the green space patches within a set range. The aggregated green space patches are then screened according to a set area threshold to obtain candidate ecological source areas.

6. The method for identifying important ecological source areas of urban green space ecological networks according to claim 5, characterized in that, The green space patches include park green space, protective green space, ancillary green space, regional green space and plaza land.

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