Method for determining spatial range of urban ecological corridor based on habits of birds under forest

Abstract

The invention relates to a method for determining a spatial range of an urban ecological corridor based on habits of birds under forest, and relates to the technical field of landscape ecology and urban ecological planning. The method comprises the following steps: acquiring a target environment variable of a preselected under-forest bird species in a target research area; inputting the target environment variable into a pre-trained species distribution model, simulating a potential suitable-for-growth area of the pre-selected under-forest bird species, and outputting a habitat suitability index; determining an ecological source space distribution range based on the potential suitable growth area; constructing a landscape transfer resistance surface based on the habitat suitability index; inputting the data of the ecological source space distribution range and the landscape transfer resistance surface into a minimum cumulative resistance model to generate a path of the urban ecological corridor; and based on the circuit theory model, determining the function connectivity in the transfer process of the preselected under-forest bird species in the urban ecological corridor path, and determining the inflection point of the change of the function connectivity as the width of the urban ecological corridor. By adopting the method, the spatial range of the urban ecological corridor can be determined.

Description

Technical Field

[0001] The present application relates to the technical fields of landscape ecology and urban ecological planning, and in particular to a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds. Background Art

[0002] Urban ecological corridors are an urban planning and design method based on ecological principles. The purpose is to create an ecological network in the urban environment to improve biodiversity, improve air quality, reduce noise and light pollution, enhance urban landscape, and improve the quality of life of urban residents. In addition, urban ecological corridors can also effectively reduce the urban heat island effect, mitigate the impact of climate change, and promote sustainable urban development. Therefore, the construction of urban ecological corridors is of great importance in urban planning and environmental protection.

[0003] The main steps of delineating urban ecological corridors are ecological source identification, landscape transfer resistance surface construction and urban ecological corridor extraction. During the delineation process, landscape transfer resistance surface construction and urban ecological corridor extraction are based on artificial experience assignment and weight setting, and mainly focus on path simulation of ecological corridors, which is of little significance for actually guiding the planning and construction of urban ecological corridors. Summary of the invention

[0004] Based on this, it is necessary to provide a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds in response to the above technical problems. The method comprises:

[0005] Obtain target environmental variables corresponding to pre-selected understory bird species in the target study area;

[0006] Inputting the target environmental variables corresponding to the pre-selected understory bird species into a pre-trained species distribution model, simulating the potential suitable habitat of the pre-selected understory bird species in the study area, and outputting the habitat suitability index of the pre-selected understory bird species;

[0007] Based on the potential suitable habitat, determine the spatial distribution range of ecological sources;

[0008] constructing a landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species;

[0009] Inputting the data of the spatial distribution range of the ecological source and the landscape transfer resistance surface into the minimum cumulative resistance model to generate the path of the urban ecological corridor;

[0010] Based on the circuit theory model, the functional connectivity of the preselected understory bird species in the urban ecological corridor path is determined during the transfer process, and the inflection point of the functional connectivity change is determined as the width of the urban ecological corridor.

[0011] As an optional implementation, the target environmental variables include bioclimatic variables, terrain variables, human activity variables and vegetation attribute variables, the terrain variables include elevation and slope, the human activity variables include population distribution density and night light index, and the vegetation attribute variables include normalized vegetation index, vegetation height, building height, land use and surface temperature.

[0012] As an optional implementation, the method further includes:

[0013] Extract the values ​​of environmental variables of several forest bird locations;

[0014] The correlation coefficient was used to test the multicollinearity between the environmental variables;

[0015] For each of the environmental variables, if the correlation between the environmental variable and other environmental variables is less than or equal to a preset correlation threshold, the bioclimatic variable is determined as the target environmental variable and used for model prediction;

[0016] If the correlation between the environmental variable and any environmental variable is greater than the correlation threshold, the environmental variable in the comparison group that is closely associated with the distribution of the pre-selected understory bird species is retained as the target environmental variable and used for model prediction.

[0017] As an optional implementation, the target environmental variables corresponding to the pre-selected understory bird species are input into a pre-trained species distribution model, the potential suitable habitat of the pre-selected understory bird species in the study area is simulated, and the habitat suitability index of the pre-selected understory bird species is output, including:

[0018] Inputting the target environmental variables corresponding to the pre-selected understory bird species into each sub-model in the species distribution model, and outputting the simulation results of each sub-model;

[0019] The simulation results that meet the preset screening conditions are determined as the potential suitable habitats of the pre-selected understory bird species in the study area, and the habitat suitability index of the pre-selected understory bird species is output.

[0020] As an optional embodiment, the sub-model includes one or more of a generalized linear model, a boosted regression tree, a generalized additive model, a classification tree analysis, a multivariate adaptive regression spline function, an artificial neural network, a flexible discriminant analysis, a random forest and a maximum entropy model.

[0021] As an optional implementation, the method further includes:

[0022] The simulation results of each sub-model were tested by receiver operating characteristic curve, true skill statistic and Kappa coefficient;

[0023] For each of the sub-models, if the receiver operating characteristic, true skill statistic and Kappa coefficient of the simulation results of the sub-model are the highest, the simulation results of the sub-model meet the preset screening conditions.

[0024] As an optional implementation manner, the determining of the spatial distribution range of the ecological source based on the potential suitable habitat includes:

[0025] Dividing the potential suitable areas into unsuitable areas, low suitable areas, medium suitable areas and high suitable areas;

[0026] Based on the preset spatial processing method, the highly suitable area is spatially processed to determine the spatial distribution range of the ecological source.

[0027] As an optional implementation, the preset spatial processing method is GIS spatial processing, and the GIS spatial processing includes one or more of feature merging, feature splitting and feature elimination.

[0028] As an optional implementation manner, constructing a landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species includes:

[0029] The inverse of the habitat suitability index of the pre-selected understory bird species is determined as the landscape transfer resistance surface.

[0030] As an optional implementation, the circuit theory model is used to determine the functional connectivity of the preselected understory bird species in the urban ecological corridor during the transfer process, and the inflection point of the functional connectivity is determined as the width of the urban ecological corridor, including:

[0031] Divide the urban ecological corridor path to create a number of buffer zones;

[0032] Based on the circuit theory model, extracting the functional connectivity of the pre-selected understory bird species during the transfer process after circuit theory quantification;

[0033] The inflection point of the functional connectivity in the urban ecological corridor path is calculated using the piecewise linear regression method, and the buffer size when the inflection point appears is used as the width of the urban ecological corridor.

[0034] The present application provides a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds. The technical solution provided by the embodiments of the present application brings at least the following beneficial effects: obtaining target environmental variables corresponding to pre-selected understory bird species in a target study area; inputting the target environmental variables corresponding to the pre-selected understory bird species into a pre-trained species distribution model, simulating the potential suitable habitat of the pre-selected understory bird species in the study area, and outputting the habitat suitability index of the pre-selected understory bird species; determining the spatial distribution range of an ecological source based on the potential suitable habitat; constructing a landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species; inputting the data of the spatial distribution range of the ecological source and the landscape transfer resistance surface into a minimum cumulative resistance model to generate a path of an urban ecological corridor; determining the functional connectivity of the pre-selected understory bird species in the path of the urban ecological corridor during the transfer process based on a circuit theory model, and determining the inflection point of the change in the functional connectivity as the width of the urban ecological corridor. This application quantifies the spatial relationship between forest birds and their living environment factors, and uses circuit theory to quantify the spatial distribution of functional connectivity of forest birds' potential movement probability in high-density cities. The emergence of inflection points in urban ecological corridors means that the component elements inside the corridor have undergone a sudden change. Changes in the elements of the suitable habitat for forest birds will make it more difficult for them to survive, rest or migrate in this area, thereby determining the scope of the urban ecological corridor.

[0035] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flowchart of a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application;

[0038] Figure 2 A flowchart of an environmental variable screening method provided in an embodiment of the present application;

[0039] Figure 3 A flowchart of a potential suitable area simulation method provided in an embodiment of the present application;

[0040] Figure 4 A flowchart of a simulation result evaluation method provided in an embodiment of the present application;

[0041] Figure 5 A flowchart of a method for determining the spatial distribution range of an ecological source provided in an embodiment of the present application;

[0042] Figure 6 A flowchart of another method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application;

[0043] Figure 7 A flowchart of an example of a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application;

[0044] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] The following will describe in detail a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application in conjunction with a specific implementation method. Figure 1 A flowchart of a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the specific steps are as follows:

[0047] Step 101, obtaining target environmental variables corresponding to pre-selected understory bird species in a target study area.

[0048] In implementation, the target study area may be a city where an urban ecological corridor is to be planned, and forest insectivorous birds (abbreviated as forest birds) are selected as indicator species for planning urban ecological corridors for the following reasons: First, forest birds adapt to life in urban environments, but still face reproduction and foraging pressures brought about by urbanization. Secondly, forest birds are evenly and widely distributed in the study area. Finally, forest birds are sensitive to the connectivity of green spaces and are considered to be indicator species for balancing urban food webs. The present application embodiment selects yellow-bellied tits, red-billed leiothrixes and gray-backed shrikes as pre-selected forest bird species. For the pre-selected forest bird species, the target environmental variables corresponding to the pre-selected forest bird species are obtained through a pre-selected database and / or field information collection.

[0049] Optionally, the target environmental variables include bioclimatic variables, topographic variables, human activity variables and vegetation attribute variables, topographic variables include elevation and slope, human activity variables include population distribution density and night light index, and vegetation attribute variables include normalized vegetation index, vegetation height, building height, land use and surface temperature.

[0050] Optionally, in order to avoid interference from irrelevant environmental variables and facilitate model interpretation, the target environmental variables obtained in step 101 may be screened. Figure 2 A flowchart of an environmental variable screening method provided in an embodiment of the present application, such as Figure 2 As shown, the details are as follows:

[0051] Step 201, extracting the values ​​of environmental variables of several locations where birds under the forest are located.

[0052] In practice, in environmental analysis and ecological modeling, bioclimatic variables are important factors in determining species distribution. These variables can include temperature, precipitation, humidity, etc., which indirectly affect the ecosystem by affecting the living conditions of plants and animals. The values ​​of these variables at the locations of understory birds can be extracted from the study area through GIS (Geographic Information System) software, which provides data support for the construction and analysis of subsequent models. For example: 144 locations were selected in City A, and these locations were evenly distributed throughout the area. Technicians used GIS software to extract bioclimatic variables such as temperature, precipitation, and sunshine hours at these locations, and organized these data into a matrix form for subsequent analysis and model construction.

[0053] Step 202: Use correlation coefficients to test for multicollinearity between environmental variables.

[0054] In practice, multicollinearity refers to the phenomenon of high correlation between two or more independent variables. In ecological modeling, if there is a strong correlation between multiple environmental variables, it may lead to model instability and difficult to interpret results. Therefore, it is necessary to evaluate the multicollinearity between these variables through correlation coefficient test to ensure the independence and reliability of the model.

[0055] Step 203: for each environmental variable, if the correlation between the environmental variable and other environmental variables is less than or equal to a preset correlation threshold, the bioclimatic variable is determined as a target environmental variable and used for model prediction.

[0056] In the implementation, for each environmental variable, its correlation with other environmental variables is checked. If the correlation between an environmental variable and all other variables is lower than a preset threshold (such as 0.7 or 0.8), it is considered that the correlation between this variable and other variables is low and can independently represent an environmental feature. Environmental variables with low correlation with other environmental variables are identified as target environmental variables. Using these target environmental variables for model construction and prediction, the prediction performance of the model is better and easier to interpret because the problem of multicollinearity is reduced.

[0057] Step 204: if the correlation between the environmental variable and any environmental variable is greater than the correlation threshold, the environmental variable in the comparison group that is closely associated with the distribution of the pre-selected understory bird species is retained as the target environmental variable and used for model prediction.

[0058] In implementation, among highly correlated variables, retaining variables that are more closely associated with the target species distribution can reduce model complexity and improve model interpretability. For example, the correlation coefficient (Sperman) can be used to test multicollinearity between bioclimatic variables. Among a set of highly correlated environmental variables (e.g., R>0.8), only one variable that is closely associated with species distribution or easy to explain by the model is selected for model prediction. Among them, the resolution of the raster data can be 10m, and the coordinate system can use the WGS-1984 coordinate system. For example, the selected environmental variables include average annual temperature, annual precipitation, maximum summer temperature, minimum winter temperature, and vegetation coverage. The Pearson correlation coefficient between each variable can be calculated to determine the correlation. The correlation between the average annual temperature and the maximum summer temperature is 0.85, which is higher than the set threshold (e.g., 0.8), and the correlations between other environmental variables are all lower than 0.8. Because the correlation between the average annual temperature and the maximum summer temperature is higher than the threshold, an environmental variable needs to be selected between the two. Based on species distribution or for easy model interpretation, the average annual temperature can be selected as the target environmental variable because it can represent the average temperature throughout the year. Other environmental variables that meet the requirements, such as annual precipitation, minimum winter temperature, and vegetation coverage, are also selected as target environmental variables. Using the selected target environmental variables to build prediction models such as random forest models, the prediction results of the models are more accurate, and the contribution of each target environmental variable to the results is easier to explain.

[0059] Step 102, input the target environmental variables corresponding to the pre-selected forest bird species into the pre-trained species distribution model, simulate the potential suitable habitat of the pre-selected forest bird species in the study area, and output the habitat suitability index of the pre-selected forest bird species.

[0060] In practice, the Biomod2 software package can be used to perform machine learning modeling on pre-selected understory bird species. The trained species distribution models (SDMs) can simulate the potential suitable habitats of understory birds in the study area based on the input target environmental variables. These suitable habitats are areas where understory birds may inhabit and reproduce. The habitat suitability index (HSI) is obtained through simulation, and the higher the value, the more suitable the environment.

[0061] Optional, Figure 3 A flowchart of a potential suitable area simulation method provided in an embodiment of the present application is as follows: Figure 3 As shown, in step 102, the target environmental variables corresponding to the pre-selected understory bird species are input into the pre-trained species distribution model, the potential suitable habitat of the pre-selected understory bird species in the study area is simulated, and the specific steps of outputting the habitat suitability index of the pre-selected understory bird species are as follows:

[0062] Step 301, input the target environmental variables corresponding to the pre-selected understory bird species into each sub-model in the species distribution model, and output the simulation results of each sub-model.

[0063] In practice, the species distribution model (SDM) can predict the potential habitat of the target species in the study area by inputting environmental variables. Usually, a single model may have bias or limitations, so the use of multiple sub-models (such as random forest, maximum entropy model, generalized linear model, etc.) can comprehensively utilize the advantages of different models to improve the robustness and accuracy of the prediction results. These sub-models each output simulation results, providing a basis for subsequent potential habitat analysis.

[0064] Optionally, in the process of modeling the species distribution model, except for the maximum entropy model that uses the ENMeval program package to adjust parameters (such as: adjusting the frequency multiple RM=0.5, and the feature combination is LQ) and then modeling, the remaining sub-models all use the Biomod2 default model parameter settings. In the modeling process, the data of several distribution points of the indicator species (urban forest birds) are randomly selected and divided into a training data set (such as 75%) and a test data set (such as 25%). In addition, in order to meet the Biomod2 modeling requirements and better simulate the actual distribution of urban forest birds, the embodiment of the present application can randomly generate several pseudo-missing points to participate in the modeling. In order to avoid the errors caused by a single modeling, each model repeats the above process a preset number of times, and finally generates a modeling result.

[0065] Step 302, determine the simulation results that meet the preset screening conditions as potential suitable habitats for pre-selected understory bird species in the study area, and output the habitat suitability index of the pre-selected understory bird species.

[0066] In practice, the prediction results of different sub-models may vary, so it is necessary to set screening conditions to select the results that best meet the research needs. The screening conditions can include the accuracy indicators of the model (such as the receiver operating characteristic curve AUC, the true skill statistic TSS and the Kappa coefficient, etc.), or the degree of fit with the actual observation data. The screened results will be used to determine the potential suitable habitats for understory birds and output the habitat suitability index (HSI) as an indicator to describe the environmental suitability of different regions.

[0067] Optionally, the sub-models in the species distribution model include one or more of generalized linear models, boosted regression trees, generalized additive models, classification tree analysis, multivariate adaptive regression spline functions, artificial neural networks, flexible discriminant analysis, random forests, and maximum entropy models.

[0068] Optional, Figure 4 A flowchart of a simulation result evaluation method provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the criteria for judging the screening conditions in step 302 are as follows:

[0069] Step 401 , the simulation results of each sub-model are tested by using the receiver operating characteristic curve, true skill statistics and Kappa coefficient.

[0070] In practice, some statistical indicators are needed to evaluate the predictive performance of the species distribution model. These indicators include the area under the receiver operating characteristic curve (ROC curve) (AUC), the true skill statistic (TSS), and the Kappa coefficient. AUC can measure the model's ability to distinguish between positive and negative examples. The higher the value, the better the model performance. 1.0 is an ideal model and 0.5 is a random model. TSS takes into account the sensitivity and specificity of the model. The value range is -1 to 1. The larger the value, the better the model prediction. The Kappa coefficient can measure the degree of fit between the model classification results and the random classification. The higher the Kappa coefficient, the more reliable the model's prediction results. These indicators jointly evaluate the performance of the model in different dimensions, so as to comprehensively judge the pros and cons of the model.

[0071] Step 402: for each sub-model, if the receiver operating characteristic, true skill statistic and Kappa coefficient of the simulation result of the sub-model are the highest, then the simulation result of the sub-model meets the preset screening condition.

[0072] In implementation, after evaluating multiple sub-models, screening out the optimal model is the key to ensuring the accuracy of species distribution prediction. By comparing the AUC, TSS, and Kappa coefficients of each model, the best performing model can be identified. A model with high indicators means that it performs better than other models in all dimensions and can more accurately predict the distribution of species. The model that meets these preset screening conditions is considered to be the best, and its simulation results will be used for the final habitat suitability analysis. For example, the AUC, TSS, and Kappa coefficients of the random forest model are the highest, and AUC (0.982±0.013,), TSS (0.992±0.005), and ROC (0.972±0.026) meet the preset screening conditions. Therefore, the simulation results of the random forest model can be used as the final potential suitable area prediction results for subsequent habitat suitability analysis and ecological corridor planning.

[0073] Optionally, if the indicators of other models are also close or higher, the models can be combined or the simulation results can be superimposed to improve the reliability of the overall prediction.

[0074] Step 103, based on the potential suitable habitat, determine the spatial distribution range of the ecological source.

[0075] In implementation, the spatial distribution range of ecological sources can be determined based on potential suitable areas.

[0076] Optional, Figure 5 A flowchart of a method for determining the spatial distribution range of an ecological source provided in an embodiment of the present application, such as Figure 5 As shown, the specific steps of determining the spatial distribution range of the ecological source based on the potential suitable habitat in step 103 are as follows:

[0077] Step 501, divide the potential suitable area into unsuitable area, low suitable area, medium suitable area and high suitable area.

[0078] In practice, the division of potential suitable areas is to analyze the habitat suitability of species in different environments in a more detailed manner. By dividing the suitable areas into unsuitable areas, low suitability areas, medium suitability areas and high suitability areas, the habitat quality in the area can be classified to help identify the areas most likely to support the survival and reproduction of target species. This classification is usually based on the prediction results of species distribution models (such as the habitat suitability index HSI), and the boundaries of each suitable area are determined by setting different thresholds. For example: A habitat suitability index (HSI) map of the target study area was generated based on the random forest model. According to the HSI value, the target study area can be divided into four levels: unsuitable area (HSI < 0.2), low suitability area (0.2 ≤ HSI < 0.5), medium suitability area (0.5 ≤ HSI < 0.7) and high suitability area (HSI ≥ 0.7). For example, high-value suitable areas are concentrated in the city's A outer ring greenway or around various parks, accounting for 13.5% of the target research area, low-value suitable areas account for 15.1%, medium-value suitable areas account for 5.0%, and unsuitable areas account for 66.4%. Based on the above data, it can be concluded that in highly urbanized areas, forest birds mainly inhabit large urban green spaces or areas with less human interference.

[0079] Step 502, based on a preset spatial processing method, spatial processing is performed on the high suitability area to determine the spatial distribution range of the ecological source.

[0080] In implementation, after identifying the high suitability area, further spatial processing is required to determine the specific spatial distribution range of the ecological source. Preset spatial processing methods (such as feature merging, feature splitting, and removal of isolated patches) can be used to ensure the connectivity and suitability of the ecological source, and ultimately determine the distribution of the ecological source that is most conducive to species survival. For example: After demarcating the high suitability area, these areas were spatially processed using GIS software. Specifically, adjacent high suitability area patches were first merged to form a larger continuous area to increase the connectivity of the habitat. Then, small, isolated high suitability area patches were removed because these patches may not be able to support the survival of understory bird populations in the long term. Finally, a series of larger and connected high suitability areas were identified as ecological sources, and the spatial distribution range of these areas was recorded.

[0081] Optionally, the preset spatial processing method is GIS spatial processing, and GIS spatial processing includes one or more of feature merging, feature splitting and feature elimination.

[0082] Step 104: construct a landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species.

[0083] In implementation, the landscape transfer resistance surface can be constructed based on the habitat suitability index of pre-selected understory bird species.

[0084] Optionally, in step 104, the method for constructing the landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species is: determining the inverse of the habitat suitability index of the pre-selected understory bird species as the landscape transfer resistance surface.

[0085] In practice, the landscape transfer resistance surface is used in ecological corridor planning to describe the resistance encountered by species when moving in different landscapes. Areas with a high habitat suitability index (HSI) generally mean lower resistance because these areas are more suitable for species survival and movement. Conversely, areas with a low HSI represent higher resistance. In order to quantify this resistance, the reciprocal of the HSI can be used as a landscape transfer resistance surface: the higher the HSI value, the lower the resistance; the lower the HSI value, the higher the resistance. In this way, a resistance surface that reflects the difficulty of species moving in the landscape can be constructed for simulating and optimizing ecological corridors. For example, a habitat suitability index (HSI) map can be generated based on the results of the species distribution model output. The HSI value is distributed between 0 and 1, where 1 represents the highest suitability and 0 represents unsuitability. In order to construct the landscape transfer resistance surface, the reciprocal of the HSI can be used as the resistance value of each grid cell.

[0086] Optionally, the landscape transfer resistance surface can describe the obstacles or barriers encountered by species or individuals in the process of movement and migration under different geographical environments, thereby providing a better understanding and guidance for the connectivity and species protection of ecosystems. These obstacles can be terrain, land use type, artificial facilities, natural obstacles, etc., which will affect the distribution, diffusion and migration of species. In the prior art, land use / cover, terrain and environmental variables that can quantify human activities (population density distribution, night light index, etc.) are mostly considered as the synthetic elements of the landscape resistance surface, while less attention is paid to variables in three-dimensional space such as vegetation and buildings, and there is even less research on the impact of surface temperature and various bioclimatic variables on the migration of urban forest birds. The embodiment of the present application can construct a landscape transfer resistance surface based on the characteristics of urban forest birds themselves and their adaptation and response to the habitat, which can avoid the problem of low accuracy caused by inadequate consideration of environmental factors in the prior art.

[0087] Optionally, a linear landscape resistance surface can be constructed based on the importance of environmental factors as a comparison item, and compared with the landscape transfer resistance surface (the inverse of the habitat suitability index) simulated by the nonlinear relationship of each environmental variable in the embodiment of the present application for verification. The average resistance of the linear landscape resistance surface after normalization is 0.466, and the standard deviation is 0.087. The average resistance of the landscape transfer resistance surface simulated by the nonlinear relationship is 0.464, and the standard deviation is 0.086. By comparing the two types of landscape resistance surface construction methods, the landscape transfer resistance surface simulated by the nonlinear relationship can correct the overestimation of the impact of environmental variables on the transfer of indicator species, and the actual spatial distribution is basically consistent with the linear landscape resistance surface method, and there is no particularly large spatial differentiation. Therefore, the embodiment of the present application uses the inverse of the habitat suitability index as the landscape transfer resistance surface for understory birds.

[0088] Step 105, inputting the data of the spatial distribution range of ecological sources and the landscape transfer resistance surface into the minimum cumulative resistance model to generate the path of the urban ecological corridor.

[0089] In implementation, Linkage Mapper is a tool that integrates the minimum cumulative resistance model (MCR), circuit theory, and graph theory. The Linkage Pathways Tool based on Linkage Mapper can generate paths for urban ecological corridors based on the spatial distribution range of ecological sources and data on landscape transfer resistance surfaces.

[0090] Step 106, based on the circuit theory model, determine the functional connectivity of the pre-selected understory bird species in the urban ecological corridor path during the transfer process, and determine the inflection point of the functional connectivity change as the width of the urban ecological corridor.

[0091] In implementation, the circuit theory model can be used to simulate the movement of species in urban ecological corridors. The embodiment of the present application quantifies the ecological connectivity and species migration ability (i.e., functional connectivity) of urban forest birds in various areas of urban ecological corridors through the circuit theory model. Linkage Mapper, a GIS platform plug-in extended from the circuit theory model, can call Circuitscape4.0 to quantify the functional connectivity of urban forest birds in different geographical environments, and integrates the minimum cost path (LCP) to generate potential transfer paths for urban forest birds. Furthermore, by identifying the inflection point of the change in functional connectivity of forest birds within the urban ecological corridor during the transfer process, the width of the urban ecological corridor, that is, the minimum ideal width, can be determined.

[0092] Optional, Figure 6 A flowchart of another method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application is shown in FIG. Figure 6As shown, in step 106, based on the circuit theory model, the functional connectivity of the preselected understory bird species in the urban ecological corridor path during the transfer process is determined, and the inflection point of the functional connectivity is determined as the width of the urban ecological corridor. The specific steps are as follows:

[0093] Step 601, divide the urban ecological corridor path and create a number of buffer zones.

[0094] In implementation, when determining the functional connectivity of ecological corridors, it is first necessary to divide the urban ecological corridor paths into several sections for more detailed analysis. Each section of the path corresponds to a buffer zone, which is used to analyze the movement and connection characteristics of species in different areas. By dividing the path, the contribution of each section of the path to the overall functional connectivity can be more accurately evaluated, and changes in connectivity can be identified. For example: a proposed urban ecological corridor path was selected, and the GIS tool was used to divide the path according to a 500m fishing net, and the corresponding buffer was created. The width of the buffer was set to 10-500 meters based on preliminary analysis, and the width should not exceed the size of the fishing net. These buffers will be used for subsequent functional connectivity analysis to help evaluate the mobility of understory birds in different path areas within the urban ecological corridor.

[0095] Step 602 , based on the circuit theory model, extract the functional connectivity of the pre-selected understory bird species during the transfer process after being quantified by the circuit theory.

[0096] In implementation, the circuit theory model treats the landscape as a circuit network, and evaluates the ability of species to move in the landscape by simulating the flow of current in the network. Functional connectivity reflects the strength of connection between species in different landscape units, which is affected by resistance surfaces and landscape structure. Through the circuit theory model, the functional connectivity of understory birds in each buffer zone can be quantified, showing the movement potential and obstacles of species in the corridor path. For example: Circuitscape4.0 software can be used for analysis, and the previously constructed landscape transfer resistance surface and ecological source area are used as input to generate quantitative results of the functional connectivity of the study area. High connectivity areas indicate that understory birds move smoothly in the buffer zone, while low connectivity areas may have obstacles to species movement. If certain sections of the urban ecological corridor have high functional connectivity, these areas may be key ecological corridor sections.

[0097] Step 603, using the piecewise linear regression method to calculate the inflection point of the functional connectivity in the urban ecological corridor path, and taking the width of the buffer zone on both sides of the corridor path when the inflection point of the urban ecological corridor appears as the width of the urban ecological corridor.

[0098] In practice, there may be critical points or inflection points in the change of functional connectivity along the corridor path, representing a significant change in species connectivity. The position of these inflection points can be determined by the piecewise linear regression method, and the width setting of the corridor can be optimized accordingly. In some areas, the inflection point of connectivity may mean that a wider corridor is needed to ensure the smooth movement of species. Therefore, the buffer size corresponding to the inflection point can be used as an important basis for determining the width of the corridor. For example, the piecewise linear regression method provided by the Segments package of the R language platform can be used to calculate the inflection point of the functional connectivity decline of each urban ecological corridor, and the size of the buffer range on both sides of the corridor path when the inflection point appears is used as the width of the ecological corridor. Specifically, the piecewise linear regression method provided by the Segments package of the R language platform can be used to calculate the inflection point of the functional connectivity decline of each urban ecological corridor, and the width of the buffer on both sides of the corridor path when the inflection point appears is used as the width of the ecological corridor. Specifically, Circuitspace4.0 is used to evaluate the functional connectivity and range of forest birds in the overall urban ecological corridor transfer in the target study area. Then, using the ArcGIS buffer tool, we set different buffer widths (such as 10 to 500 meters, increasing in sequence) to obtain different buffer ranges, count the functional connectivity in each buffer, and use the piecewise linear regression model to fit the functional connectivity trend of the corridor under different buffers to find the turning point of the decline. That is, when the functional connectivity of the urban ecological corridor turns, it indicates that its internal component elements have undergone major changes and cannot effectively support the normal transfer, habitat and other life and survival behaviors of understory birds. Therefore, we set the buffer width when the turning point appears as the width of the urban ecological corridor.

[0099] Optional, Figure 7 This is a flowchart of an example of a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds provided in an embodiment of the present application, such as Figure 7 As shown, the specific steps are as follows:

[0100] Step 701: Select understory bird species in the target study area and use these species as indicator species.

[0101] Step 702: Determine target environmental variables corresponding to pre-selected understory bird species, including bioclimatic variables, terrain variables, human activity variables, and vegetation attribute variables.

[0102] Step 703: Input the target environmental variables corresponding to the pre-selected forest bird species into the species distribution model (SMDs model) to simulate and determine the potential suitable habitat and habitat suitability index of the species in the target study area.

[0103] Step 704: Based on the output of the SMDs model, identify and divide the potential suitable habitats of understory bird species.

[0104] Step 705: Based on the potential suitable habitat, determine the spatial distribution range of the ecological source, indicating the main habitat area of ​​the understory bird species in the urban ecosystem.

[0105] Step 706: Take the inverse of the habitat suitability index and use it to construct a landscape migration resistance surface to represent the degree of resistance of each area to the migration of understory bird species.

[0106] Step 707: Input the spatial distribution range of the ecological source and the landscape transfer resistance surface into the minimum cumulative resistance (MCR) model to determine the optimal path of the urban ecological corridor.

[0107] Step 708: Generate a specific path of the urban ecological corridor through MCR model calculation.

[0108] Step 709: Based on the circuit theory model, analyze the migration process of forest bird species in the urban ecological corridor and determine the functional connectivity of the migration.

[0109] Step 710: Further analyze the functional connectivity of different areas in the ecological corridor path and identify the inflection point of the connectivity.

[0110] Step 711: Use the piecewise linear regression method to calculate the inflection point of the functional connectivity, and determine the width of the urban ecological corridor based on the inflection point, and apply it to the actual ecological corridor planning.

[0111] The embodiment of the present application provides a method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds. The technical solution provided by the embodiment of the present application at least brings the following beneficial effects: obtaining the target environmental variables corresponding to the pre-selected understory bird species in the target study area; inputting the target environmental variables corresponding to the pre-selected understory bird species into the pre-trained species distribution model, simulating the potential suitable habitat of the pre-selected understory bird species in the study area, and outputting the habitat suitability index of the pre-selected understory bird species; determining the spatial distribution range of the ecological source based on the potential suitable habitat; constructing the landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species; inputting the data of the spatial distribution range of the ecological source and the landscape transfer resistance surface into the minimum cumulative resistance model to generate the path of the urban ecological corridor; determining the functional connectivity of the pre-selected understory bird species in the transfer process in the path of the urban ecological corridor based on the circuit theory model, and determining the inflection point of the functional connectivity change as the width of the urban ecological corridor. The present application quantifies the spatial relationship between understory birds and the environmental factors in which they live, and uses circuit theory to quantify the spatial distribution of the functional connectivity of the potential movement probability of understory birds in high-density cities. The emergence of an inflection point in an urban ecological corridor means that the component elements within the corridor have undergone a mutation. The changes in the elements of the suitable habitat for understory birds will make it more difficult for them to survive, rest or migrate in this area, thereby determining the scope of the urban ecological corridor.

[0112] It should be understood that although Figures 1 to 7 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 1 to 7 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0113] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0114] In one embodiment, a computer device is provided, such as Figure 8 As shown, it includes a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor implements the above-mentioned method steps for determining the spatial scope of the urban ecological corridor based on the habits of understory birds when executing the computer program.

[0115] In one embodiment, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for determining the spatial scope of an urban ecological corridor based on the habits of understory birds.

[0116] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0117] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0118] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0119] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0120] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for determining the spatial scope of urban ecological corridors based on the habits of understory birds, characterized in that: The method comprises: Obtain the target environmental variables corresponding to the pre-selected understory bird species in the target study area; Inputting the target environmental variables corresponding to the pre-selected understory bird species into a pre-trained species distribution model, simulating the potential suitable habitat of the pre-selected understory bird species in the study area, and outputting the habitat suitability index of the pre-selected understory bird species; Based on the potential suitable habitat, determine the spatial distribution range of ecological sources; constructing a landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species; Inputting the data of the spatial distribution range of the ecological source and the landscape transfer resistance surface into the minimum cumulative resistance model to generate the path of the urban ecological corridor; Based on the circuit theory model, the functional connectivity of the preselected understory bird species in the urban ecological corridor path is determined during the transfer process, and the inflection point of the functional connectivity change is determined as the width of the urban ecological corridor.

2. The method according to claim 1, characterized in that: The target environmental variables include bioclimatic variables, terrain variables, human activity variables and vegetation attribute variables. The terrain variables include elevation and slope. The human activity variables include population distribution density and night light index. The vegetation attribute variables include normalized vegetation index, vegetation height, building height, land use and surface temperature.

3. The method according to claim 2, characterized in that The method further comprises: Extract the values ​​of environmental variables of several forest bird locations; The correlation coefficient was used to test the multicollinearity between the environmental variables; For each of the environmental variables, if the correlation between the environmental variable and other environmental variables is less than or equal to a preset correlation threshold, the bioclimatic variable is determined as the target environmental variable and used for model prediction; If the correlation between the environmental variable and any environmental variable is greater than the correlation threshold, the environmental variable in the comparison group that is closely associated with the distribution of the pre-selected understory bird species is retained as the target environmental variable and used for model prediction.

4. The method according to claim 1, characterized in that The target environmental variables corresponding to the pre-selected understory bird species are input into a pre-trained species distribution model, the potential suitable habitat of the pre-selected understory bird species in the study area is simulated, and the habitat suitability index of the pre-selected understory bird species is output, including: Inputting the target environmental variables corresponding to the pre-selected understory bird species into each sub-model in the species distribution model, and outputting the simulation results of each sub-model; The simulation results that meet the preset screening conditions are determined as the potential suitable habitats of the pre-selected understory bird species in the study area, and the habitat suitability index of the pre-selected understory bird species is output.

5. The method according to claim 4, characterized in that The sub-models include one or more of a generalized linear model, a boosted regression tree, a generalized additive model, a classification tree analysis, a multivariate adaptive regression spline function, an artificial neural network, a flexible discriminant analysis, a random forest, and a maximum entropy model.

6. The method according to claim 4, characterized in that The method further comprises: The simulation results of each sub-model were tested by receiver operating characteristic curve, true skill statistic and Kappa coefficient; For each of the sub-models, if the receiver operating characteristic, true skill statistic and Kappa coefficient of the simulation results of the sub-model are the highest, the simulation results of the sub-model meet the preset screening conditions.

7. The method according to claim 1, characterized in that Determining the spatial distribution range of ecological sources based on the potential suitable habitat includes: Dividing the potential suitable areas into unsuitable areas, low suitable areas, medium suitable areas and high suitable areas; Based on the preset spatial processing method, the highly suitable area is spatially processed to determine the spatial distribution range of the ecological source.

8. The method according to claim 7, characterized in that The preset spatial processing method is GIS spatial processing, and the GIS spatial processing includes one or more of feature merging, feature splitting and feature elimination.

9. The method according to claim 1, characterized in that: The step of constructing a landscape transfer resistance surface based on the habitat suitability index of the pre-selected understory bird species includes: The inverse of the habitat suitability index of the pre-selected understory bird species is determined as the landscape transfer resistance surface.

10. The method according to claim 1, characterized in that The circuit theory model is used to determine the functional connectivity of the preselected understory bird species in the urban ecological corridor path during the transfer process, and the inflection point of the functional connectivity is determined as the width of the urban ecological corridor, including: Divide the urban ecological corridor path to create a number of buffer zones; Based on the circuit theory model, extracting the functional connectivity of the pre-selected understory bird species during the transfer process after circuit theory quantification; The inflection point of the functional connectivity in the urban ecological corridor path is calculated using the piecewise linear regression method, and the buffer size when the inflection point appears is used as the width of the urban ecological corridor.

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