Method and device for evaluating influence of road on ecological network, computer equipment and medium

Abstract

The invention relates to the technical field of ecological networks, and discloses a method and device for evaluating the influence of a road on an ecological network, computer equipment and a medium, and the method comprises the steps: obtaining basic geographic information data of a research region, and carrying out the preprocessing of the basic geographic information data; based on the preprocessed basic geographic information data, setting a plurality of scenes according to resistance surface superposition conditions of different grades of roads; constructing an ecological network corresponding to a plurality of different diffusion distance thresholds for each scene; evaluating the connectivity of the ecological network from a landscape scale, a plaque scale and a corridor scale by using a graph theory index and a circuit theory index to obtain a multi-scale ecological network connectivity evaluation result; based on a multi-scale ecological network connectivity assessment result, the influence degree of the road grade on the ecological network is compared and analyzed, and the problem that the influence of comprehensive assessment of the road grade on the landscape connectivity of the ecological network on multiple scales is not considered in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of ecological network technology, and in particular to a method, device, computer equipment and medium for evaluating the impact of roads on ecological networks. Background Art

[0002] Building ecological networks or restoring landscape connectivity of ecological networks is considered one of the most effective solutions for conserving biodiversity in fragmented landscapes. Landscape connectivity refers to the extent to which the landscape promotes or hinders species movement and other forms of ecological flow between resource habitat patches. Good landscape connectivity can promote species persistence, gene flow and other key ecological processes. Conversely, the loss of landscape connectivity may lead to population isolation, decline in species abundance and inbreeding, thereby increasing the risk of species extinction. In addition, trophic cascades of large species extinctions can combine with other environmental changes to affect the biodiversity of terrestrial systems.

[0003] Landscape connectivity is affected and limited not only by landscape pattern and species dispersal capacity, but also by landscape and environmental changes. Influencing factors include land use and land cover change, hydroclimatic changes, urban morphological evolution, and road development. The rapid expansion of roads in various places and their profound ecological impacts have attracted scholars to study the impact of roads on landscape connectivity. These studies are usually conducted in terms of structural connectivity (landscape pattern) or functional connectivity (species response to landscape pattern). Changes in structural connectivity caused by roads are usually quantified by landscape pattern indices or spatial analysis methods that describe physical changes in habitat shape, fragmentation, and continuity. However, it is difficult to characterize species' perception and interaction of landscape structure and heterogeneity through structural connectivity. Therefore, attempts have been made to analyze the impact of roads on various species (e.g., birds or mammals; mammals with different dispersal capacities) from the perspective of functional connectivity to predict the impact on the abundance and distribution of target species. Related technologies have also attempted to analyze the potential ecological impact of roads on protected areas, or to guide the planning and design of roads, crossings, and protected areas. However, these studies have mainly focused on high-level roads and pre-planned roads, and rarely considered the impact of different levels of roads on landscape connectivity. In fact, hierarchical roads have different properties, including width, traffic volume, and permeability, which have different effects on wildlife species and landscape patterns. Therefore, further revealing the impact of hierarchical roads on landscape connectivity is an extremely important research topic.

[0004] Landscape connectivity changes caused by roads occur at multiple scales. How to integrate multi-scale assessments of the impact of road grade on the landscape connectivity of ecological networks is an urgent problem that needs to be solved. Summary of the invention

[0005] In view of this, the present invention provides a method, device, computer equipment and medium for evaluating the impact of roads on ecological networks, so as to solve the problem that the prior art does not consider the comprehensive evaluation of the impact of road grades on the landscape connectivity of ecological networks at multiple scales.

[0006] In the first aspect, the present invention provides a method for assessing the impact of roads on ecological networks, the method comprising: acquiring basic geographic information data of a study area, and preprocessing the basic geographic information data; based on the preprocessed basic geographic information data, setting a plurality of scenarios according to the superposition of resistance surfaces of roads of different grades; constructing ecological networks corresponding to a plurality of different diffusion distance thresholds for each scenario; using graph theory indexes and circuit theory indexes to assess the connectivity of the ecological network from landscape scale, patch scale and corridor scale, respectively, to obtain a multi-scale ecological network connectivity assessment result; based on the multi-scale ecological network connectivity assessment result, comparing and analyzing the degree of impact of road grade on the ecological network.

[0007] The method for assessing the impact of roads on ecological networks provided by the present invention processes basic geographic information data of a study area, sets multiple scenarios based on the preprocessed basic geographic information data according to the superposition of resistance surfaces of roads of different grades, constructs ecological networks corresponding to multiple different diffusion distance thresholds for each scenario, and uses graph theory indexes and circuit theory indexes to assess the connectivity of the ecological network from landscape scale, patch scale and corridor scale, respectively, to obtain multi-scale ecological network connectivity assessment results, and compares and analyzes the impact of road grades on the ecological network based on the multi-scale ecological network connectivity assessment results, thereby achieving an assessment of the impact of quantitative grade roads on the connectivity of species with different diffusion capabilities at the landscape, patch and corridor scales, providing a useful reference for the protection of balanced regional biodiversity and traffic planning, and solving the problem that the prior art does not consider the comprehensive assessment of the impact of road grades on the landscape connectivity of ecological networks at multiple scales.

[0008] In an optional embodiment, the basic geographic information data includes land use / cover type data, digital elevation model, normalized vegetation index and road data; the basic geographic information data is preprocessed, including: performing unified projection transformation processing on the land use / cover type data, digital elevation model, normalized vegetation index and road type data to form a spatial data set with consistent spatial reference coordinates, and performing grading and road buffer analysis on the road data.

[0009] By performing unified projection conversion on land use / cover type data, digital elevation models, and normalized vegetation index, as well as grading and road buffer analysis on road data, a data basis is provided for subsequent analysis of the impact of roads on ecological networks.

[0010] In an optional embodiment, based on the preprocessed basic geographic information data, multiple scenarios are set according to the superposition of resistance surfaces of roads of different levels, including: setting a baseline scenario in which roads have no effect on species movement based on the preprocessed basic geographic information data; superimposing the resistance surface of the highest-level road and its buffer zone in the baseline scenario to form a first comparable scenario, and superimposing the resistance surface of the second-highest-level road and its buffer zone in the first comparable scenario to form a second comparable scenario, until the resistance surfaces of all levels of roads and their buffer zones are superimposed to form all comparable scenarios, wherein the multiple scenarios set include the baseline scenario and all comparable scenarios.

[0011] By setting a baseline scenario and superimposing multiple comparable scenarios of resistance surfaces of roads of different grades, comparable scenarios are provided for subsequent analysis of the impacts of roads of different grades on ecological networks.

[0012] In an optional embodiment, an ecological network corresponding to a plurality of different diffusion distance thresholds is constructed for each scenario, including: determining diffusion distance thresholds of a plurality of target species for each scenario; extracting habitat patches and calculating the cumulative resistance surface for each scenario based on the preprocessed basic geographic information data; and constructing an ecological network corresponding to a plurality of different diffusion distance thresholds for each scenario using a minimum cumulative resistance model based on the determined plurality of diffusion distance thresholds, habitat patches and cumulative resistance surfaces.

[0013] By constructing ecological networks corresponding to a variety of different diffusion distance thresholds for each scenario based on the determined multiple diffusion distance thresholds, habitat patches and the cumulative resistance surface using the minimum cumulative resistance model, the impact of different levels of roads on the connectivity of ecological networks of various distances can be quantified from multiple scales, which is conducive to balancing regional biodiversity and road traffic planning.

[0014] In an optional embodiment, based on the preprocessed basic geographic information data, habitat patches are extracted and the cumulative resistance surface of each scenario is calculated, including: using the land use / cover type data in the preprocessed basic geographic information data to extract forest land of a preset scale as habitat patches; extracting multiple influencing factors from the preprocessed basic geographic information data, and calculating the cumulative resistance surface of each scenario based on the preset resistance value and weight value of each influencing factor, wherein the resistance influencing factors include one or more of land use / cover type data, slope, elevation, vegetation coverage, distance from the city, distance from settlements, road grade and distance from the road.

[0015] By calculating the cumulative resistance surface for each scenario based on the preset resistance value and weight value of each resistance influencing factor, a comprehensive resistance surface can be obtained.

[0016] In an optional embodiment, the graph theory index includes a possible connectivity index, a patch betweenness centrality index, a patch importance index, a corridor minimum cost path length, a corridor cost-weighted distance and a corridor quality index; the circuit theory index includes a corridor redundancy and a current centrality index; the connectivity assessment result of the ecological network includes overall landscape connectivity, habitat patch connectivity, corridor connectivity, key habitat patch identification results and key corridor identification results; correspondingly, the graph theory index and the circuit theory index are used to evaluate the connectivity of the ecological network from the landscape scale, the patch scale and the corridor scale, respectively, to obtain a multi-scale ecological network connectivity assessment result, including: at the landscape scale, calculating the possible connectivity index of the ecological network based on the graph theory principle, and using the possible connectivity index to evaluate the overall landscape connectivity of the ecological network under different scenarios and different diffusion distances; at the patch scale, calculating the patch betweenness centrality index and the patch betweenness centrality index based on the graph theory principle. The patch importance index is used to evaluate the degree to which a single habitat patch participates in the minimum cost path between all other patch pairs in the complete landscape under different scenarios and different diffusion distances based on the patch betweenness centrality index. The importance of a single habitat patch as a connecting element or stepping stone in promoting connectivity between other patches is evaluated based on the patch importance index. The patch comprehensive index is calculated based on the patch betweenness centrality index and the patch importance index. The key habitat patches in the ecological network under different scenarios and different diffusion distances are identified through the patch comprehensive index. At the corridor scale, the corridor minimum cost path length, corridor cost-weighted distance, corridor quality index, corridor redundancy and current centrality index are calculated based on graph theory and circuit theory. The corridor connectivity in the ecological network is evaluated based on the corridor minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy. The key corridors in the ecological network under different scenarios and different diffusion distances are identified through the current centrality index.

[0017] The present invention calculates different indexes from the landscape scale, patch scale and corridor scale respectively, and correspondingly evaluates the overall landscape connectivity, habitat patch connectivity and corridor connectivity in ecological networks of different scenarios, and identifies the corresponding key patches and key corridors. It can then quantify the impact of different levels of roads on the connectivity of ecological networks of various distances from multiple scales, and can integrate multiple scales to analyze the impact of road grades on ecological networks.

[0018] In an optional implementation, based on the multi-scale ecological network connectivity assessment results, the degree of influence of road grade on the ecological network is compared and analyzed, including: at the landscape scale, comparing the possible connectivity index of each ecological network under different scenarios and diffusion distances, and analyzing the degree of influence of road grade on the overall connectivity of the ecological network according to the change amount and change rate of the possible connectivity index of each ecological network; at the patch scale, comparing the habitat patch betweenness centrality index, patch importance index and key habitat patch ... The influence of road grade on habitat patches is analyzed by the change amount and rate of the betweenness centrality index and patch importance index of the network and the change of key habitat patches; at the corridor scale, the minimum cost path length, corridor cost-weighted distance, corridor quality index, corridor redundancy and key corridors in each ecological network under different scenarios and diffusion distances are compared, and the influence of road grade on corridors is analyzed according to the change amount and rate of the minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy of each ecological network and the change of key corridors.

[0019] By comparing the changes in the overall landscape connectivity, habitat patch betweenness centrality index, patch importance index, minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy of each ecological network under different scenarios and diffusion distances at various scales, as well as the changes in key patches and key corridors, the impact of hierarchical roads on the ecological network can be comprehensively evaluated from multiple angles.

[0020] In a second aspect, the present invention provides a device for assessing the impact of roads on ecological networks, the device comprising: a data acquisition and processing module, used to acquire basic geographic information data of a study area and preprocess the basic geographic information data; a scenario setting module, used to set a variety of scenarios based on the preprocessed basic geographic information data and the superposition of resistance surfaces of roads of different grades; an ecological network construction module, used to construct ecological networks corresponding to a variety of different diffusion distance thresholds for each scenario; a connectivity assessment module, used to use graph theory indexes and circuit theory indexes to assess the connectivity of the ecological network from landscape scale, patch scale and corridor scale, respectively, to obtain a multi-scale ecological network connectivity assessment result; an impact degree analysis module, used to compare and analyze the impact of road grade on the ecological network based on the multi-scale ecological network connectivity assessment result.

[0021] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for assessing the impact of roads on ecological networks according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for assessing the impact of roads on ecological networks according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 is a schematic diagram of a process for evaluating the impact of roads on ecological networks according to an embodiment of the present invention;

[0025] Figure 2 is a flow chart of another method for assessing the impact of a road on an ecological network according to an embodiment of the present invention;

[0026] Figure 3 is a schematic diagram of possible changes in connectivity index of an ecological network under different scenarios and different diffusion distance thresholds according to an embodiment of the present invention;

[0027] FIG4( a ) is a schematic diagram showing the change in the sum of plaque betweenness centrality indexes of all plaques in different scenarios and different diffusion distance thresholds according to an embodiment of the present invention;

[0028] FIG4( b ) is a schematic diagram showing the change in the sum of the plaque importance indexes of all plaques under different scenarios and different diffusion distance thresholds according to an embodiment of the present invention;

[0029] FIG5( a ) is a schematic diagram of the patch betweenness centrality index of a single habitat patch under different scenarios and different diffusion distance thresholds according to an embodiment of the present invention;

[0030] FIG5( b ) is a schematic diagram of the plaque importance index of a single plaque under different scenarios and different diffusion distance thresholds according to an embodiment of the present invention;

[0031] FIG6( a ) is a schematic diagram showing changes in the sum of the minimum cost path lengths LCP of corridors under different diffusion distance thresholds in different scenarios according to an embodiment of the present invention;

[0032] FIG6( b ) is a schematic diagram showing the change in the total corridor cost weighted distance CWD for different diffusion distance thresholds in different scenarios according to an embodiment of the present invention;

[0033] FIG6( c ) is a schematic diagram showing the change in the total of the corridor quality index CWD_to_LCP for different diffusion distance thresholds in different scenarios according to an embodiment of the present invention;

[0034] FIG6( d ) is a schematic diagram showing changes in the sum of corridor redundancy ERs under different diffusion distance thresholds in different scenarios according to an embodiment of the present invention;

[0035] FIG. 7( a ) is a schematic diagram of the spatial distribution of ecological networks, key habitat patches, and key corridors under a diffusion distance threshold of 10 km in six scenarios according to an embodiment of the present invention;

[0036] FIG7( b ) is a schematic diagram of the spatial distribution of ecological networks, key habitat patches, and key corridors under a diffusion distance threshold of 15 km in six scenarios according to an embodiment of the present invention;

[0037] FIG7( c ) is a schematic diagram of the spatial distribution of ecological networks, key habitat patches, and key corridors under a diffusion distance threshold of 20 km in six scenarios according to an embodiment of the present invention;

[0038] FIG7( d ) is a schematic diagram of the spatial distribution of ecological networks, key habitat patches, and key corridors under a diffusion distance threshold of 25 km in six scenarios according to an embodiment of the present invention;

[0039] Figure 8 is a schematic diagram of local changes in key corridor space under different scenarios and different diffusion distances according to an embodiment of the present invention;

[0040] Fig. 9 is a structural block diagram of a device for evaluating the impact of roads on ecological networks according to an embodiment of the present invention;

[0041] Fig.10 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] Changes in landscape connectivity caused by roads occur at multiple scales. At the patch scale, roads may lead to changes in intrapatch connectivity (e.g., habitat patch area, quality) and interpatch connectivity (e.g., diffusion amount, degree of connection between habitat patches or stepping stone habitat patches). At the corridor level, the barrier effect of roads may increase the difficulty of species migration or change the migration routes of species within the landscape. Because connectivity can be interconnected and interact at multiple scales, the above impacts may extend to the entire landscape, leading to changes in biodiversity and ecosystems in the region. There are few methods in the related art that couple the landscape-patch-corridor scales. An embodiment of the present invention provides a method for assessing the impact of roads on ecological networks. Through scenario simulation and comparative analysis, the degree of influence of roads of different levels on the migration of species with different diffusion capabilities is clarified, and landscape connectivity assessment is incorporated into road traffic planning to build animal-friendly infrastructure.

[0044] According to an embodiment of the present invention, an embodiment of a method for assessing the impact of roads on ecological networks is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] In an embodiment of the present invention, a method for evaluating the impact of roads on ecological networks is provided, which can be used in computers, servers, etc. Figure 1 is a flow chart of a method for evaluating the impact of roads on ecological networks according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0046] Step S101, obtaining basic geographic information data of the study area and preprocessing the basic geographic information data.

[0047] Specifically, the study area is a specific geographical area, which can refer to a city, an ecosystem or any geographical unit.

[0048] like Figure 2As shown in the figure, basic geographic information data include land use / cover type data (Land Use and LandCover, LULC), digital elevation model data (Digital Elevation Model, DEM) and normalized difference vegetation index (Normalized Difference Vegetation Index, NDVI) and road data. Among them, land use / cover type data include 7 categories: cultivated land, forest land, grassland, garden, water area, urban land, and bare land; digital elevation model data can extract elevation and slope data; normalized vegetation index can extract vegetation coverage data; road data is classified according to road type, road width, and driving speed attributes, from high to low, they are railway, expressway, urban expressway, main road, and secondary road.

[0049] In order to unify the above data, the land use / cover type data, digital elevation model data, normalized difference vegetation index and road data are preprocessed as follows:

[0050] The land use / cover type data, digital elevation model, normalized vegetation index and road type data are uniformly projected and transformed to form a spatial dataset with consistent spatial reference coordinates, and the road data are graded and analyzed for road buffer zones.

[0051] By performing unified projection conversion on land use / cover type data, digital elevation models, and normalized vegetation index, as well as grading and road buffering analysis on road data, a data basis is provided for subsequent analysis of the impact of roads on ecological networks.

[0052] Step S102, based on the pre-processed basic geographic information data, multiple scenarios are set according to the superposition of resistance surfaces of roads of different grades.

[0053] Specifically, scenario comparison analysis will help to better understand the changes in connectivity of ecological networks at multiple scales. Therefore, different scenarios are set based on basic geographic information data, and the differences between scenarios are mainly reflected by road data. For example, in the baseline scenario, the idealized assumed road is not an obstacle, but a favorable landscape for species migration. In order to distinguish the impact of different levels of roads on the spread of species, certain resistance values ​​are assigned to different levels of roads and their buffer zones in different scenarios. In the baseline scenario, the resistance value of the road is assigned the lowest value of 1. Taking the baseline scenario as the benchmark, different levels of roads are superimposed as different comparable scenarios. For example, one of the comparable scenarios superimposes the resistance surface of the railway and its buffer zone, and the other comparable scenario superimposes the resistance surface of the highway and its buffer zone on this basis, so as to analyze the impact of different levels of roads on the ability of species to spread and migrate.

[0054] Step S103 , constructing ecological networks corresponding to a plurality of different diffusion distance thresholds for each scenario.

[0055] For each scenario, multiple different diffusion distance thresholds are set, corresponding to the diffusion capacity of the species. For example, the diffusion distance threshold can be set to 10km, 15km, 20km, and 25km. The short diffusion distance threshold is 10km, the medium diffusion distance threshold is 15km and 20km, and the long diffusion distance threshold is 25km.

[0056] When the road grade is 5, there are 5 comparable scenarios, plus the baseline scenario, for a total of 6 scenarios. When 4 diffusion distance thresholds are set for each scenario, there are 24 different diffusion distance thresholds for different scenarios, that is, there are 24 ecological networks constructed. The setting of different scenarios and different diffusion distance thresholds takes into account the impact of different grades of roads on species with different diffusion capabilities.

[0057] Step S104, using graph theory index and circuit theory index, the connectivity of the ecological network is evaluated from the landscape scale, patch scale and corridor scale respectively to obtain a multi-scale ecological network connectivity evaluation result.

[0058] Specifically, in a broad sense, landscape refers to the comprehensive topographical features of a certain geographical area, including the complex composed of land, space and materials on the land.

[0059] Habitat patches are the basic building blocks of landscape pattern, areas with relatively consistent environmental characteristics in the landscape that are essential for the survival and ecological functions of specific species.

[0060] A corridor, also known as an ecological corridor, refers to a channel in the landscape that can connect spatially isolated and scattered habitat patches. It is used to promote ecological processes such as species diffusion, migration and exchange, and is an important part of building a complete regional ecological network of mountains, rivers, forests, fields, lakes and grasslands.

[0061] The overall connectivity of the ecological network can be evaluated at the landscape scale, the connectivity between habitat patches in the ecological network can be evaluated at the patch scale, and the connectivity between corridors in the ecological network can be evaluated at the corridor scale. Based on the overall connectivity in the ecological network, the connectivity between habitat patches, and the connectivity between corridors, the three scales of landscape, patch, and corridor can be integrated to comprehensively evaluate the connectivity of the ecological network and obtain multi-scale ecological network connectivity assessment results.

[0062] Step S105 , based on the multi-scale ecological network connectivity assessment results, comparative analysis is performed on the impact of road grades on the ecological network.

[0063] Since the road resistance surfaces superimposed in each scenario are different, the differences in the connectivity of the ecological networks constructed based on each scenario and diffusion distance threshold can be seen as the result of the influence of different road conditions. Based on this, the degree of influence of road grade on the ecological network can be analyzed according to the differences in the connectivity assessment results of different ecological networks, and the impact of different grades of roads on the biological migration of species with different diffusion abilities can be simulated.

[0064] The method for assessing the impact of roads on ecological networks provided by the present invention processes basic geographic information data of a study area, sets multiple scenarios based on the preprocessed basic geographic information data according to the superposition of resistance surfaces of roads of different grades, constructs ecological networks corresponding to multiple different diffusion distance thresholds for each scenario, and uses graph theory indexes and circuit theory indexes to assess the connectivity of the ecological network from landscape scale, patch scale and corridor scale, respectively, to obtain multi-scale ecological network connectivity assessment results, and compares and analyzes the impact of road grades on the ecological network based on the multi-scale ecological network connectivity assessment results, thereby achieving an assessment of the impact of quantitative grade roads on the connectivity of species with different diffusion capabilities at the landscape, patch and corridor scales, providing a useful reference for the protection of balanced regional biodiversity and traffic planning, and solving the problem that the prior art does not consider the comprehensive assessment of the impact of road grades on the landscape connectivity of ecological networks at multiple scales.

[0065] In some embodiments, step S102, based on the pre-processed basic geographic information data, multiple scenarios are set according to the superposition of resistance surfaces of roads of different grades, including:

[0066] Step S1021, setting a baseline scenario in which roads have no effect on species movement based on the preprocessed basic geographic information data.

[0067] Specifically, a baseline scenario (Scenario 1, S1) was set based on LULC data, DEM data, and NDVI data. In the baseline scenario, it was assumed that the road was not an obstacle but a favorable landscape for species migration. In order to distinguish the diffusion capacity of different scenarios on species, a certain resistance value was assigned to the set scenarios. In the baseline scenario where it was assumed that the road was not an obstacle but a favorable landscape for species migration, the resistance value of the road was assigned the lowest value of 1.

[0068] Step S1022, superimposing the resistance surface of the highest-level road and its buffer zone in the baseline scenario to form a first comparable scenario, superimposing the resistance surface of the second-highest-level road and its buffer zone in the first comparable scenario to form a second comparable scenario, until the resistance surfaces of all levels of roads and their buffer zones are superimposed to form all comparable scenarios, wherein the multiple scenarios set include the baseline scenario and all comparable scenarios.

[0069] In one example, there are 6 scenarios, of which 5 are comparable scenarios. In the baseline scenario, the highest-level road and its buffer zone are superimposed to form the first comparable scenario (Scenario2, S2), that is, the first comparable scenario S2 is generated by superimposing the resistance surface of the railway and its buffer zone on the baseline scenario S1. In the first comparable scenario, the second-highest-level road and its buffer zone are superimposed to form the second comparable scenario (Scenario3, S3), that is, the second comparable scenario S3 is generated by superimposing the resistance surface of the highway and its buffer zone on the first comparable scenario S2. The third comparable scenario (Scenario4, S4) is generated by superimposing the resistance surface of the urban expressway and its buffer zone on the basis of the second comparable scenario S3. The fourth comparable scenario (Scenario5, S5) is generated by superimposing the resistance surface of the main road and its buffer zone on the basis of the third comparable scenario S4. The fifth comparable scenario (Scenario6, S6) is generated by superimposing the resistance surface of the secondary road and its buffer zone on the basis of the fourth comparable scenario S5. In the comparable scenarios S2 to S6, roads were considered as obstacles to species movement and were assigned a maximum resistance value of 1000, and the buffer zone was assigned a corresponding resistance value based on the distance decay theory. By setting a baseline scenario and superimposing multiple comparable scenarios of resistance surfaces of roads of different levels, comparable scenarios were provided for the subsequent analysis of the impact of roads of different levels on the ecological network.

[0070] Furthermore, in step S103, ecological networks corresponding to a plurality of different diffusion distance thresholds are constructed for each scenario, including:

[0071] Step S1031, determining diffusion distance thresholds of multiple target species for each scenario.

[0072] Specifically, target species are usually divided into specific species and virtual species. Specific species are usually endangered animals living in protected areas. The ecological network constructed and the evaluated connectivity based on the characteristics of the habitat area, body size and diffusion ability of specific species can characterize its ecological process, but it is not enough to represent the ecological flow of most species in the area. Therefore, in order to maximize the effectiveness of ecological network protection, the target species are determined by the method of agnostic species, and virtual terrestrial mammals are usually selected as target species, which are parameterized based on specific species groups. Therefore, the embodiment of the present invention selects virtual mammals as target species to cover most of the medium and large wild mammals in the study area. Virtual mammals have a large habitat area requirement and a long diffusion distance.

[0073] Four different diffusion distance thresholds were set for each scenario, namely 10km, 15km, 20km and 25km, to characterize species with different diffusion capabilities, which represent the diffusion capabilities of species from low to high. That is, the maximum diffusion distances of virtual mammals are 10km, 15km, 20km and 25km, which represent the diffusion capabilities of species from low to high.

[0074] Step S1032, based on the pre-processed basic geographic information data, extract habitat patches and calculate the cumulative resistance surface of each scenario.

[0075] Specifically, step S1032 includes:

[0076] Step S10321, using the land use / cover type data in the pre-processed basic geographic information data to extract forest land of a preset scale as a habitat patch.

[0077] Habitat patches are areas that support or have the potential to support the daily activities of wild species. The preset scale includes but is not limited to 50 hectares, 100 hectares, 200 hectares, etc. In the embodiment of the present invention, forest land with an area greater than or equal to 100 hectares is selected as a habitat habitat patch. The selection of forest land with an area greater than or equal to 100 hectares as a habitat habitat patch is to ensure that the minimum habitat patch has sufficient carrying capacity and to facilitate large-scale cross-administrative district analysis. Based on land use type data, two or more connected forest lands are regarded as one habitat patch, while forest lands divided by roads are regarded as multiple habitat patches. In the embodiment of the present invention, a total of 110 habitat patches are determined, accounting for 64.66% of the total forest area. The distribution of habitat patches has spatial heterogeneity. Large habitats are mainly distributed in the south and north of the study area, and a large number of small habitat patches are distributed in the middle.

[0078] Step S10321, extract multiple influencing factors from the preprocessed basic geographic information data, and calculate the cumulative resistance surface of each scenario based on the preset resistance value and weight value of each influencing factor, wherein the influencing factors include one or more of land use / cover type data, slope, elevation, vegetation coverage, distance from the city, distance from residential areas, road grade and distance from the road.

[0079] The cumulative resistance surface represents the permeability of the landscape level to species movement. Landscapes that are conducive to species movement (such as woodlands) have low or no resistance, while landscapes that are not conducive to species movement (such as construction land) have high resistance.

[0080] According to references and expert opinions, the embodiment of the present invention selects 8 influencing factors to calculate the cumulative resistance surface: divided into 6 basic factors and 2 variable factors. The basic factors include: land use / cover type, slope, elevation, normalized vegetation index, distance from the city, and distance from residential areas. The variable factors include different levels of roads and distances from roads.

[0081] Resistance value setting: Classify each influencing factor and set the corresponding resistance value. Among them, the road buffer reflects the barrier effect, which decays with the increase of the distance from the road. In order to distinguish the effects of different levels of roads and road buffers on the diffusion capacity of species, different resistance values ​​and weight values ​​are assigned to different levels of roads and road buffers. The resistance values ​​and weight values ​​of different levels of roads and buffers are shown in Table 1 below.

[0082] Table 1

[0083]

[0084]

[0085] The resistance values ​​and weight values ​​of the basic influencing factors in the basic geographic information data are shown in Table 2 below.

[0086] Table 2

[0087]

[0088]

[0089] The weight values ​​of influencing factors are set according to expert opinions, as shown in Tables 1 and 2: the range is [0, 1000]. A lower resistance value indicates that the species is easy to move, and a higher resistance value indicates that the movement is restricted. Distance factors such as distance to the city, distance to the settlement, and distance to the road are analyzed by the road buffer distance to the road to represent human interference. The resistance value decays with increasing distance. Large water bodies (such as the Yangtze River, Han River, and Ba River) are set to a maximum resistance value of 1000 because the water surface is wide and it is difficult for mammals to pass during migration. On this basis, the resistance surface of each factor is summarized through a multi-factor comprehensive evaluation method, and the cumulative resistance surface of different scenarios is output. The calculation formula is as follows:

[0090]

[0091] Among them, R final is the value of the cumulative resistance surface, R i is the resistance value of factor i, W i is the weight value of influencing factor i.

[0092] Step S1033, based on the determined multiple diffusion distance thresholds, habitat patches and cumulative resistance surfaces, the minimum cumulative resistance model is used to construct ecological networks corresponding to multiple different diffusion distance thresholds for each scenario.

[0093] Specifically, AcrGIS10.0 (geographic information software) and Linkage Mapper 2.0 (GIS tool for supporting regional wildlife habitat connectivity analysis) were used to construct ecological networks using the minimum cumulative resistance model. The software can identify the path with the lowest cost (i.e., the path with the least resistance) on the cumulative resistance surface between habitat patches according to the setting of the diffusion distance threshold, thereby constructing the corresponding ecological network. 24 ecological networks were constructed using 6 different scenarios and 4 different diffusion distance thresholds for each scenario, as shown in Figure 7(a), Figure 7(b), Figure 7(c), and Figure 7(d).

[0094] The embodiment of the present invention constructs a baseline scenario and a comparable scenario by superimposing the resistance surfaces of roads of different grades, sets multiple diffusion distance thresholds for the baseline scenario and each comparable scenario based on the diffusion capacity of the target species, clarifies the impact of different grades of roads on the migration of species with different diffusion capacities, and incorporates landscape connectivity assessment into road traffic planning to reduce the adverse effects of road construction and alleviate the conflict between biodiversity conservation and road traffic planning.

[0095] In an embodiment of the present invention, a method for evaluating the impact of roads on ecological networks is provided, which can be used in computers, servers, etc. The method includes the following steps:

[0096] Step S201, obtain basic geographic information data of the study area and pre-process the basic geographic information data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0097] Step S202: Based on the pre-processed basic geographic information data, multiple scenarios are set according to the superposition of resistance surfaces of roads of different grades. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0098] Step S203: construct ecological networks corresponding to multiple different diffusion distance thresholds for each scenario. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0099] Step S204, using graph theory index and circuit theory index, the connectivity of the ecological network is evaluated from the landscape scale, patch scale and corridor scale respectively to obtain the connectivity evaluation results of each ecological network.

[0100] Specifically, the graph theory indices include possible connectivity index, patch betweenness centrality index, patch importance index, corridor minimum cost path length, corridor cost-weighted distance and corridor quality index; the circuit theory indices include corridor redundancy and current centrality index; the connectivity assessment results of the ecological network include overall landscape connectivity, habitat patch connectivity, corridor connectivity, key habitat patch identification results and key corridor identification results.

[0101] Correspondingly, the above step S204 includes:

[0102] Step S2041, at the landscape scale, the possible connectivity index of the ecological network is calculated based on the principles of graph theory, and the possible connectivity index is used to evaluate the overall landscape connectivity of the ecological network under different scenarios and different diffusion distances.

[0103] Specifically, at the landscape scale, the overall functional connectivity of the ecological network under different diffusion distances in six scenarios, namely the overall landscape connectivity, is analyzed using graph theory principles. Figure 3 As shown. The Probability of Connectivity (PC), one of the graph theory indices, is used to evaluate the overall landscape connectivity at the landscape level. It represents the probability that two species randomly placed in the landscape fall into interconnected habitat areas. The calculation formula is as follows:

[0104]

[0105] Where n is the number of habitat patches in the landscape; a i and a j are the areas of habitat patches i and j respectively; A L is the total landscape area (the total landscape area in the embodiment of the present invention refers to the area of ​​the entire study area); is the maximum product probability of all possible paths between habitat patches i and j (including single-step paths). In the embodiment of the present invention,

[0106] Step S2042: at the patch scale, the patch betweenness centrality index and the patch importance index are calculated based on the principles of graph theory. The degree to which a single habitat patch participates in the minimum cost path between all other patch pairs in the complete landscape under different scenarios and different diffusion distances is evaluated based on the patch betweenness centrality index. The importance of a single habitat patch as a connecting element or stepping stone in promoting connectivity between other patches is evaluated based on the patch importance index. The patch comprehensive index is calculated based on the patch betweenness centrality index and the patch importance index. The key habitat patches under different scenarios and different diffusion distances are identified through the patch comprehensive index.

[0107] Specifically, the plaque betweenness centrality index (BC k) and the plaque importance index (dPCconnector k ) to evaluate the connectivity of each habitat patch to compare and analyze whether the road level affects the contribution of all habitat patches and a single habitat patch to the entire ecological network. Finally, a new index, the comprehensive patch index (BC k _dPCconnector k ) to identify key habitat patches in ecological networks and determine the priority of protection. As shown in Figure 4(a), Figure 4(b), Figure 5(a), Figure 5(b), Figure 7(a), Figure 7(b), Figure 7(c) and Figure 7(d). Wherein: Scenarios in Figure 4(a) and Figure 4(b) represent scenarios, and Patches in Figure 5(a) and Figure 5(b) represent habitat patches. In Figure 7(a), Figure 7(b), Figure 7(c) and Figure 7(d), Key nodes represent key nodes, Other nodes represent other nodes, Key corridors represent key corridors, Other corridors represent other corridors, Study Area represents study area, and Patches represents habitat patches.

[0108] The patch betweenness centrality index assesses the extent to which habitat patch k participates in the minimum cost path between all other habitat patch pairs in the complete landscape and is calculated as follows:

[0109]

[0110] In the formula, g ij (k) is the total number of minimum cost paths between habitat patch i and habitat patch j passing through habitat patch k, g ij is the total number of minimum cost paths between habitat patch i and habitat patch j.

[0111] Patch Importance Index dPCconnector k It measures the importance of habitat patch k as a connecting element or intermediate stepping stone to the connectivity between other habitat patches. k Different, dPCconnector k It measures the change in connectivity caused by removing habitat patch k from the landscape. The patch importance index depends only on the topological position of habitat patch k in the ecological network and has nothing to do with the properties of habitat patch k. k Habitat patches with high values ​​indicate high importance and cannot be completely replaced by other habitat patches in the landscape. The patch importance index is calculated as follows:

[0112]

[0113] Among them, the plaque importance index dPCconnector k is the importance of habitat patch k to maintaining habitat connectivity in the landscape, PCconnector is the importance value of the original complete landscape containing k habitat patches, PCconnector remove,k is the importance value of all habitat patches in the landscape after removing k.

[0114] Plaque composite index BC k _dPCconnector k Used to identify key habitat patches in ecological networks. The patch comprehensive index integrates the patch betweenness centrality index and the patch importance index. The patch betweenness centrality index and the patch importance index are both unique and complementary, and can describe different aspects and roles of habitat patches in the network. The calculation formula is as follows:

[0115]

[0116] Usually in biodiversity conservation and road traffic planning, the resources allocated to protect the connectivity of the ecological network are relatively scarce and limited, so the top 10% of the patch comprehensive index value is selected as the key habitat patch. Therefore, the key habitat patch determined in the embodiment of the present invention is not only in a relatively central position in the ecological network, but also an important provider of ecological network connectivity.

[0117] The connectivity of habitat patches in ecological networks of different scenarios was evaluated through the patch betweenness centrality index, patch importance index and key habitat patches, and key habitat patches were identified based on the patch comprehensive index. The key habitat patches of ecological networks of different scenarios identified have a certain robustness and are of high importance in different scenarios, providing a reference for biodiversity conservation. The contribution of key habitat patches to the connectivity of ecological networks is irreplaceable. Strengthening their protection may achieve greater benefits in responding to landscape changes or climate change.

[0118] Step S2043, at the corridor scale, the minimum cost path length, the corridor cost weighted distance, the corridor quality index, the corridor redundancy and the current centrality index of the corridor are calculated based on graph theory and circuit theory, the corridor connectivity in the ecological network is evaluated based on the minimum cost path length, the corridor cost weighted distance, the corridor quality index and the corridor redundancy, and the key corridors in the ecological network under different scenarios and different diffusion distances are identified through the current centrality index.

[0119] Specifically, at the corridor scale, the corridor connectivity index based on graph theory and circuit theory is used to analyze the corridor connectivity in the ecological network. The corridor connectivity index includes the corridor minimum cost path length, corridor cost weighted distance, corridor quality index and corridor redundancy, which characterize and compare the corridor length, quality and redundancy. In addition, the current centrality index (CFC) is calculated using circuit theory to quantify the importance of individual corridors and identify key corridors. As shown in Figures 6(a), 6(b), 6(c), 6(d), 7(a), 7(b), 7(c), 7(d) and Figure 8 shown. Figure 8 In the figure, Dabie and Legend represent Dabie Mountains and legend respectively.

[0120] Among them, the corridor least cost path length (LCP) and corridor cost-weighted distance (CWD) were calculated by ArcGIS and Linkage Mapper tools.

[0121] The calculation formula of corridor quality index (CWD_to_LCP) is as follows:

[0122]

[0123] In the formula, Q l is the quality index of corridor l, LCP l is the minimum cost path length of corridor l, CWD l is the corridor cost-weighted distance of the minimum cost path in corridor l. It represents the average resistance encountered by species when moving along the least-cost path between habitat patches. The larger the value, the greater the corridor resistance, the weaker the permeability, and the worse the quality. l for The reciprocal of Q l The larger the value, the better the corridor quality, and vice versa.

[0124] The redundancy of corridors is represented by the effective resistance index (ER) in circuit theory, which is calculated by ArcGIS and Linkage Mapper tools. The smaller the effective resistance of a corridor in the ecological network, the greater the redundancy, indicating that there are many alternative movement paths in addition to the minimum cost path in the ecological network.

[0125] Critical corridors are identified using the Current Flow Centrality (CFC) index based on circuit theory. The index refers to the frequency with which species in a network cross corridors. The loss of critical corridors may lead to the disconnection of certain patch pairs and a disproportionate decrease in connectivity. Therefore, corridors in the top 10% of the Current Flow Centrality index are considered critical corridors that urgently need to be protected.

[0126] Current centrality index calculation formula:

[0127]

[0128] Where s and t are the points where the current enters and flows out of the network. is the amount of current flowing through node i, and n is the number of nodes in the current network.

[0129] The connectivity of corridors in ecological networks under different scenarios was comprehensively evaluated based on the minimum cost path length of corridors, cost-weighted distance of corridors, corridor quality index, corridor redundancy and key corridors. The key corridors of ecological networks under different scenarios identified can represent ecological corridors where species migrate frequently in the ecological network, which provides conditions for the subsequent priority protection of key corridors and is beneficial to the maintenance of regional ecological network connectivity and the protection of biodiversity.

[0130] The embodiment of the present invention calculates different indexes from the landscape scale, patch scale and corridor scale respectively, and correspondingly evaluates the overall landscape connectivity, habitat patch connectivity and corridor connectivity in the ecological network of different scenarios, and identifies the corresponding key patches and key corridors, so as to quantify the impact of different levels of roads on the connectivity of ecological networks of various distances from multiple scales, and can analyze the impact of road grade on the ecological network based on multiple scales.

[0131] Step S205 , based on the multi-scale ecological network connectivity assessment results, comparative analysis is performed on the impact of road grades on the ecological network.

[0132] Specifically, the above step S205 includes:

[0133] Step S2051, at the landscape scale, compare the possible connectivity indexes of each ecological network under different scenarios and diffusion distances, and analyze the influence of road grade on the overall connectivity of the ecological network according to the change amount and change rate of the possible connectivity index of each ecological network.

[0134] Specifically, Figure 3 As shown in the figure, the change amount and change rate of the possible connectivity index (PC) under different scenarios and diffusion distances are compared to analyze the impact of road grade on the overall connectivity of the ecological network.

[0135] Step S2052, at the patch scale, compare the habitat patch betweenness centrality index, patch importance index and key habitat patches of each ecological network under different scenarios and diffusion distances, and analyze the impact of road grade on habitat patches based on the change amount and change rate of the habitat patch betweenness centrality index and patch importance index of each ecological network and the change of key habitat patches.

[0136] Specifically, as shown in Figures 4(a), 4(b), 5(a) and 5(b), the impact of road grade on habitat patches is analyzed based on the change amount and rate of change of the habitat patch betweenness centrality index and patch importance index of each ecological network and the change of key habitat patches.

[0137] Step S2053, at the corridor scale, compare the minimum cost path length, corridor cost weighted distance, corridor quality index, corridor redundancy and key corridors in each ecological network under different scenarios and diffusion distances, and analyze the impact of road grade on corridors based on the change amount and rate of change of the minimum cost path length, corridor cost weighted distance, corridor quality index and corridor redundancy in each ecological network and the change of key corridors.

[0138] Specifically, as shown in Figures 6(a), 6(b), 6(c), 6(d), 7(a), 7(b), 7(c) and 7(d), the impact of road grade on corridors is analyzed based on the changes in the minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy in each ecological network and the changes in key corridors.

[0139] By comparing the changes in the overall landscape connectivity, habitat patch betweenness centrality index, patch importance index, minimum cost path length of key habitat patches, corridor cost-weighted distance, corridor quality index, corridor redundancy and key corridors of each ecological network under different scenarios and diffusion distances at various scales, the impact of hierarchical roads on the ecological network can be comprehensively evaluated from multiple angles.

[0140] In the embodiment of the present invention, a device for evaluating the impact of roads on ecological networks is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0141] The embodiment of the present invention provides a device for evaluating the impact of roads on ecological networks. Fig. 9 As shown, including:

[0142] The data acquisition processing module 901 is used to acquire the basic geographic information data of the study area and pre-process the basic geographic information data;

[0143] A scenario setting module 902 is used to set multiple scenarios according to the superposition of resistance surfaces of roads of different grades based on the pre-processed basic geographic information data;

[0144] Ecological network construction module 903, used to construct ecological networks corresponding to multiple different diffusion distance thresholds for each scenario;

[0145] The connectivity assessment module 904 is used to assess the connectivity of the ecological network from the landscape scale, the patch scale and the corridor scale using the graph theory index and the circuit theory index, and obtain the multi-scale ecological network connectivity assessment result;

[0146] The impact degree analysis module 905 is used to compare and analyze the impact degree of road grade on the ecological network based on the multi-scale ecological network connectivity assessment results.

[0147] The device for evaluating the impact of roads on ecological networks provided by the present invention processes basic geographic information data of a study area, sets a plurality of scenarios based on the preprocessed basic geographic information data according to the superposition of resistance surfaces of roads of different grades, constructs ecological networks corresponding to a plurality of different diffusion distance thresholds for each scenario, and uses graph theory index and circuit theory index to evaluate the connectivity of the ecological network from the landscape scale, patch scale and corridor scale, respectively, to obtain a multi-scale ecological network connectivity evaluation result. Based on the multi-scale ecological network connectivity evaluation result, the degree of influence of road grade on the ecological network is compared and analyzed, and the evaluation of the impact of quantitative grade roads on the connectivity of species with different diffusion capabilities at the landscape, patch and corridor scales is realized, which provides a useful reference for the protection of balanced regional biodiversity and traffic planning, and solves the problem that the prior art does not consider the comprehensive evaluation of the impact of road grade on the landscape connectivity of ecological networks at multiple scales.

[0148] In some optional implementations, the basic geographic information data includes land use / cover type data, digital elevation model, normalized difference vegetation index and road data; the data acquisition processing module 901 includes:

[0149] The data preprocessing module is used to perform unified projection transformation processing on land use / cover type data, digital elevation model, normalized vegetation index and road type data to form a spatial data set with consistent spatial reference coordinates, and to perform classification and road buffer zone analysis on road data.

[0150] In some optional implementations, the scenario setting module 902 includes:

[0151] A baseline scenario setting module is used to set a baseline scenario in which roads have no effect on species movement based on preprocessed basic geographic information data;

[0152] A comparable scenario setting module is used to superimpose the resistance surface of the highest-level road and its buffer zone in the baseline scenario to form a first comparable scenario, and to superimpose the resistance surface of the second-highest-level road and its buffer zone in the first comparable scenario to form a second comparable scenario, until all levels of roads and their buffer zones are superimposed to form all comparable scenarios, wherein the multiple scenarios set include the baseline scenario and all comparable scenarios.

[0153] In some optional implementations, the ecological network construction module 903 includes:

[0154] a threshold determination module to determine the dispersal distance thresholds for multiple target species for each scenario;

[0155] An extraction and calculation module is used to extract habitat patches and calculate the cumulative resistance surface of each scenario based on the preprocessed basic geographic information data;

[0156] The network determination module is used to construct ecological networks corresponding to multiple different diffusion distance thresholds for each scenario using the minimum cumulative resistance model based on the determined multiple diffusion distance thresholds, habitat patches and cumulative resistance surfaces.

[0157] In some optional implementations, the extraction calculation module includes:

[0158] A patch extraction module is used to extract forest land of a preset size as habitat patches by using land use / cover type data in the preprocessed basic geographic information data;

[0159] The cumulative resistance surface calculation module is used to extract multiple influencing factors from the preprocessed basic geographic information data, and calculate the cumulative resistance surface of each scenario based on the preset resistance value and weight value of each influencing factor, wherein the resistance influencing factors include one or more of land use / cover type data, slope, elevation, vegetation coverage, distance from the city, distance from settlements, road grade and distance from the road.

[0160] In some optional embodiments, the graph theory index includes a possible connectivity index, a patch betweenness centrality index, a patch importance index, a corridor minimum cost path length, a corridor cost-weighted distance, and a corridor quality index, the circuit theory index includes a corridor redundancy and a current centrality index, and the connectivity assessment results of the ecological network include overall landscape connectivity, habitat patch connectivity, corridor connectivity, key habitat patch identification results, and key corridor identification results;

[0161] Correspondingly, the connectivity evaluation module 904 includes:

[0162] Landscape connectivity assessment module, which is used to calculate the possible connectivity index of the ecological network at the landscape scale based on graph theory principles, and use the possible connectivity index to assess the overall connectivity of the ecological network under different scenarios and different diffusion distances;

[0163] The patch connectivity assessment module is used to calculate the patch betweenness centrality index and patch importance index at the patch scale based on the principles of graph theory. Based on the patch betweenness centrality index, the module evaluates the degree to which a single habitat patch participates in the minimum cost path between all other patch pairs in the complete landscape under different scenarios and diffusion distances. Based on the patch importance index, the module evaluates the importance of a single habitat patch as a connecting element or stepping stone to promote connectivity between other patches. Based on the patch betweenness centrality index and the patch importance index, the module calculates the patch comprehensive index, and identifies key habitat patches under different scenarios and diffusion distances through the patch comprehensive index.

[0164] The corridor connectivity assessment module is used to calculate the corridor minimum cost path length, corridor cost weighted distance, corridor quality index, corridor redundancy and current centrality index at the corridor scale based on graph theory and circuit theory. The corridor connectivity is evaluated based on the corridor minimum cost path length, corridor cost weighted distance, corridor quality index and corridor redundancy, and the key corridors of the ecological network under different scenarios and different diffusion distances are identified through the current centrality index.

[0165] In some optional implementations, the impact degree analysis module 905 includes:

[0166] The landscape impact assessment module is used to compare the possible connectivity index of each ecological network under different scenarios and diffusion distances at the landscape scale, and analyze the impact of road grade on the overall connectivity of the ecological network based on the change amount and change rate of the possible connectivity index of each ecological network;

[0167] The patch impact assessment module is used to compare the habitat patch betweenness centrality index, patch importance index and key habitat patches of each ecological network under different scenarios and diffusion distances at the patch scale, and analyze the impact of road grade on habitat patches based on the change amount and change rate of habitat patch betweenness centrality index and patch importance index of each ecological network and the change of key habitat patches;

[0168] The corridor impact assessment module is used to compare the minimum cost path length, corridor cost-weighted distance, corridor quality index, corridor redundancy and key corridors in each ecological network under different scenarios and diffusion distances at the corridor scale, and analyze the impact of road grade on corridors based on the change amount and rate of change of the minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy in each ecological network and the change of key corridors.

[0169] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0170] The road impact assessment device on the ecological network in the embodiment of the present invention is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0171] An embodiment of the present invention further provides a computer device having the device for evaluating the impact of roads on ecological networks according to the above embodiment.

[0172] See also Fig.10 , Fig.10 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.10 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 10 is taken as an example.

[0173] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0174] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0175] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0176] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0177] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig.10 The example of connecting through bus is taken in the following.

[0178] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0179] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0180] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for assessing the impact of roads on ecological networks, characterized in that: The method comprises: Obtaining basic geographic information data of the study area and preprocessing the basic geographic information data; Based on the pre-processed basic geographic information data, multiple scenarios are set according to the superposition of resistance surfaces of roads of different grades; Ecological networks corresponding to a variety of different diffusion distance thresholds were constructed for each scenario; The connectivity of the ecological network was evaluated at the landscape scale, patch scale, and corridor scale using graph theory index and circuit theory index, and the multi-scale ecological network connectivity evaluation results were obtained. Based on the multi-scale ecological network connectivity assessment results, a comparative analysis is performed on the impact of road grade on the ecological network.

2. The method according to claim 1, characterized in that The basic geographic information data includes land use / cover type data, digital elevation model, normalized difference vegetation index and road data; Preprocessing the basic geographic information data includes: The land use / cover type data, digital elevation model, normalized vegetation index and road type data are uniformly projected and transformed to form a spatial dataset with consistent spatial reference coordinates, and the road data are graded and analyzed for road buffer zones.

3. The method according to claim 2, characterized in that Based on the pre-processed basic geographic information data, multiple scenarios are set according to the superposition of resistance surfaces of roads of different grades, including: Setting a baseline scenario in which roads have no impact on species movement based on the pre-processed basic geographic information data; The resistance surface of the highest-level road and its buffer zone is superimposed on the baseline scenario to form a first comparable scenario, and the resistance surface of the second-highest-level road and its buffer zone is superimposed on the first comparable scenario to form a second comparable scenario, until all levels of roads and their buffer zones are superimposed to form all comparable scenarios, wherein the multiple scenarios set include the baseline scenario and all comparable scenarios.

4. The method according to claim 1, characterized in that: Ecological networks corresponding to various diffusion distance thresholds were constructed for each scenario, including: Dispersal distance thresholds for multiple target species were determined for each scenario; Based on the pre-processed basic geographic information data, extract habitat patches and calculate the cumulative resistance surface of each scenario; Based on the determined multiple diffusion distance thresholds, habitat patches and cumulative resistance surfaces, the minimum cumulative resistance model is used to construct ecological networks corresponding to multiple different diffusion distance thresholds for each scenario.

5. The method according to claim 4, characterized in that Based on the pre-processed basic geographic information data, habitat patches are extracted and the cumulative resistance surface of each scenario is calculated, including: Extracting a preset scale of forest land as a habitat patch by using the pre-processed land use / cover type data in the basic geographic information data; A plurality of influencing factors are extracted from the preprocessed basic geographic information data, and a cumulative resistance surface for each scenario is calculated based on a preset resistance value and a weight value of each of the influencing factors, wherein the influencing factors include one or more of land use / cover type data, slope, elevation, vegetation coverage, distance from a city, distance from a settlement, road grade, and distance from a road.

6. The method according to claim 1, characterized in that The graph theory index includes possible connectivity index, patch betweenness centrality index, patch importance index, corridor minimum cost path length, corridor cost weighted distance and corridor quality index; the circuit theory index includes corridor redundancy and current centrality index; the connectivity assessment results of the ecological network include overall landscape connectivity, habitat patch connectivity, corridor connectivity, key habitat patch identification results and key corridor identification results; Correspondingly, the graph theory index and circuit theory index are used to evaluate the connectivity of the ecological network at the landscape scale, patch scale and corridor scale, respectively, and the multi-scale ecological network connectivity evaluation results are obtained, including: At the landscape scale, the possible connectivity index of the ecological network is calculated based on the principle of graph theory, and the overall landscape connectivity of the ecological network under different scenarios and diffusion distances is evaluated using the possible connectivity index; At the patch scale, the patch betweenness centrality index and the patch importance index are calculated based on the principles of graph theory. The degree to which a single habitat patch under different scenarios and different diffusion distances participates in the minimum cost path between all other patch pairs in the complete landscape is evaluated based on the patch betweenness centrality index. The importance of a single habitat patch as a connecting element or stepping stone in promoting connectivity between other patches is evaluated based on the patch importance index. The patch comprehensive index is calculated based on the patch betweenness centrality index and the patch importance index. The key habitat patches in the ecological network under different scenarios and different diffusion distances are identified through the patch comprehensive index. At the corridor scale, the corridor minimum cost path length, corridor cost-weighted distance, corridor quality index, corridor redundancy and current centrality index are calculated based on graph theory and circuit theory. The corridor connectivity in the ecological network is evaluated based on the corridor minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy, and the key corridors in the ecological network under different scenarios and different diffusion distances are identified through the current centrality index.

7. The method according to claim 6, characterized in that Based on the multi-scale ecological network connectivity assessment results, the impact of road grade on the ecological network is compared and analyzed, including: At the landscape scale, the possible connectivity index of each ecological network under different scenarios and diffusion distances is compared, and the influence of road grade on the overall connectivity of the ecological network is analyzed according to the change amount and change rate of the possible connectivity index of each ecological network; At the patch scale, the habitat patch betweenness centrality index, patch importance index and key habitat patches of each ecological network under different scenarios and diffusion distances were compared. The influence of road grade on habitat patches was analyzed based on the change amount and change rate of habitat patch betweenness centrality index and patch importance index of each ecological network and the change of key habitat patches. At the corridor scale, the minimum cost path length, corridor cost-weighted distance, corridor quality index, corridor redundancy and key corridors in each ecological network under different scenarios and diffusion distances are compared. The impact of road grade on corridors is analyzed based on the change amount and rate of change of the minimum cost path length, corridor cost-weighted distance, corridor quality index and corridor redundancy in each ecological network and the change of key corridors.

8. A device for evaluating the impact of roads on ecological networks, characterized in that: The device comprises: A data acquisition and processing module is used to acquire basic geographic information data of the study area and pre-process the basic geographic information data; A scenario setting module, used to set multiple scenarios according to the superposition of resistance surfaces of roads of different grades based on the pre-processed basic geographic information data; The ecological network construction module is used to construct ecological networks corresponding to multiple different diffusion distance thresholds for each scenario; A connectivity assessment module is used to assess the connectivity of the ecological network from the landscape scale, patch scale and corridor scale using graph theory index and circuit theory index, and obtain a multi-scale ecological network connectivity assessment result; The impact degree analysis module compares and analyzes the impact degree of road grade on the ecological network based on the multi-scale ecological network connectivity assessment result.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for assessing the impact of roads on ecological networks as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for assessing the impact of roads on ecological networks according to any one of claims 1 to 7.

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