CN117763450B - Road network blocking effect index calculation method and system for wild animals - Google Patents
Road network blocking effect index calculation method and system for wild animals Download PDFInfo
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Abstract
The invention relates to the technical field of wild animal protection, and discloses a method and a system for calculating a barrier effect index of a road network on wild animals, which specifically comprise the following steps: acquiring road information in a living area of a wild animal, and determining the total length of a road under each road grade; determining the habitat type weight of different wild animal species under each road grade; determining the distribution probability of different wild animal species in the road and the surrounding environment under each road grade; determining the avoidance distances of different wild animal species under each road class; calculating a road blocking effect index; comprehensively considering the habitat types of different wild animal types around the key road sections and key areas, giving weight according to the habitat types, giving smaller habitat type weight to the road sections with small blocking effect to the wild animals, otherwise giving larger habitat type weight, adding more accurate calculation of the blocking effect index by the habitat type weight, and improving calculation accuracy.
Description
Technical Field
The invention relates to the technical field of wild animal protection, in particular to a method and a system for calculating a barrier effect index of a road network on wild animals.
Background
Along with the development of traffic industry, road mileage and grade are increased and improved, and adverse effects such as blocking effect on the migration and diffusion of wild animals and occupation and damage to habitat are also improved. Wild animals often traverse roads in order to obtain sufficient food and spouse, and are more susceptible to road blockage. Therefore, for the barrier influence of road engineering, what response behavior is shown by wild animals is mainly driven and interfered by which factors, and whether the connectivity structures such as tunnels, bridges and culverts of the road engineering can effectively slow down the barrier influence to realize the maintenance of population survival is one of the problems to be solved urgently by road construction institutions, and is also a foundation for ensuring the coordinated development of road engineering construction and biodiversity protection under the ecological civilization construction background.
Most of the prior art aims at a single road or a local road section, and the crushing influence on the wild animal habitat is not considered from the road network perspective; when different roads are developed and researched, only the roads are simply described, and system research on different grades of roads, different road sections, whether the roads are closed or not and the like is not performed, so that different blocking influences on wild animals by key road sections are ignored; in addition, the research on the barrier effect of the road on the wild animals is very lack, the analysis is not developed from a quantitative angle, and the barrier influence degree of the road on the wild animals cannot be scientifically evaluated and the construction of the wild animal channels is guided.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method and a system for calculating the blocking effect index of a road network on wild animals, wherein the blocking effect index of the road network on the wild animals is constructed, the preliminary attempt from qualitative to quantitative is made, and the blocking effect index of the road network on the wild animals is considered to cause fragmentation influence on habitats of the wild animals from the viewpoint of the road network.
The invention provides a method for calculating a barrier effect index of a road network to wild animals, which specifically comprises the following steps:
Step S1, obtaining road information in a living area of a wild animal, wherein the road information comprises road grade and road weight;
Step S2, determining the total length of the road under each road grade according to the road information in the step S1;
step S3, determining the habitat type weights of different wild animal types under each road grade according to the road information in the step S1;
step S4, determining the distribution probability of different wild animal species in the road and the surrounding environment of the road under each road grade according to the road information in the step S1;
step S5, determining the avoidance distances of different wild animal species under each road grade according to the road information in the step S1;
Step S6, calculating a road blocking effect index according to the road information, the total length of the road under each road grade, the habitat type weight of different wild animal species under each road grade, the avoidance distance of different wild animal species under each road grade and the distribution probability of different wild animal species in the road under each road grade and the surrounding environment thereof;
the calculation formula of the road blocking effect index is as follows:
；
Wherein: is road barrier effect index, n is road grade in wild animal living area,/> Road weight for road class i in wild animal living area,/>Is the total length of the road with the road class i in the living area of the wild animals,/>Is the avoidance distance of different wild animal species under the road with the road class i in the wild animal living area, and M is the wild animal living area,/>Is the distribution probability of different wild animal species in the road with road class i in the living area of the wild animal and the surrounding environment thereof,/>The weight value of the habitat type of different wild animal species under the road with the road class i in the living area of the wild animal.
Further, in the step S3, the habitat type weight of the different wild animal species in each road class is determined according to the road information in the step S1, and the method specifically includes the following steps:
Step S31, obtaining habitat types of habitats where different wild animal species are located under each road grade;
step S32, determining vegetation coverage degree of habitat of different wild animal species under each road level according to habitat types of habitat of different wild animal species under each road level;
And step S33, determining the weight of the habitat type of the different wild animal species under each road level according to the habitat type of the habitat of the different wild animal species under each road level and the vegetation coverage degree of the habitat of the different wild animal species under each road level.
Further, the habitat types include forests, grasslands, deserts, lakes.
Further, forests in the habitat type are classified according to vegetation coverage, including high coverage forests, medium coverage forests, and low coverage forests.
Further, the habitat type weight of the high coverage forest is 0.2, the habitat type weight of the medium coverage forest is 0.5, the habitat type weight of the medium coverage forest is 0.7, and the habitat type weight of the low coverage forest is 0.8.
Further, the ecological type of the habitat is grasslands, and the ecological type weight of the grasslands is 0.8.
Further, the lakes in the habitat type are divided according to connectivity, and include a connected lake and a closed lake, wherein the habitat type weight of the connected lake is 0, and the habitat type weight of the closed lake is 1.
Further, in the step S4, the distribution probability of different wild animal species in the road and the surrounding environment under each road class is determined according to the road information in the step S1, and the method specifically includes the following steps:
Step S41, determining environmental factor data of the road and the surrounding environment thereof under each road grade and distribution point location data of different wild animal types according to the road information in the step S1;
And step S42, inputting the road grade, the environmental factor data and the distribution point location data of different wild animal species into a species distribution model, and obtaining the distribution probability of different wild animal species in the road under each road grade.
Further, the avoidance distance refers to the blocking distance of different wild animal species under each road grade;
The step S5 is to determine the avoidance distances of different wild animal species under each road class according to the road information, and specifically comprises the following steps:
Step S51, when species entities or species traces are found on two sides of different roads under each road class, recording species names, species numbers and species types;
step S52, measuring the vertical distance between different wild animal species and the road under each road grade;
and step S53, screening out the maximum vertical distance and the minimum vertical distance between different wild animals and the road in the step S42, and calculating an average value through the maximum vertical distance and the minimum vertical distance to determine the avoidance distance of different wild animals on the road under each road grade.
According to another aspect of the present invention, there is provided a road network barrier effect index calculation system for a wild animal, the system being configured to perform the road network barrier effect index calculation method for a wild animal described above, including the following modules:
The road information acquisition module: the method is used for acquiring all road grades and road weights within the living range of the wild animals;
A first acquisition unit: the road information acquisition module is connected with the road information acquisition module and is used for acquiring the total length of the road under each road grade;
A second acquisition unit: the system comprises a road information acquisition module, a border information acquisition module and a border information processing module, wherein the road information acquisition module is connected with the road information acquisition module and is used for acquiring the weights of the types of habitats of different wild animal types under each road grade;
the third acquisition unit is connected with the road information acquisition module and is used for acquiring the distribution probability of different wild animal types in the road and the surrounding environment of the road under each road grade;
the fourth acquisition module is connected with the road information acquisition module and is used for acquiring avoidance distances of different wild animal types under each road grade;
road barrier effect index output module: and the method is used for outputting the calculation result of the road blocking effect index.
In addition, the invention provides a computer readable storage medium, and a data processing program is stored on the computer readable storage medium, and the data processing program is executed by a processor to execute the method for calculating the barrier effect index of the road network to the wild animals.
The embodiment of the invention has the following technical effects:
1. The application provides a method and a system for calculating a barrier effect index of a road network on wild animals, wherein the method constructs the barrier effect index of the road network on the wild animals, which is a preliminary attempt from qualitative to quantitative, and the road network is considered to cause crushing influence on habitat of the wild animals; when different roads are developed and researched, systematic research is carried out on different grades of roads, different road sections, whether the roads are closed or not, and the like, and the different blocking effects of important road sections on wild animals are considered in all aspects, so that the calculated blocking effect index is more accurate, and a more definite guiding effect can be provided when the actual roads are reconstructed and expanded, road network planning and wild animal channels are laid and ecological corridor construction is carried out.
2. The invention provides a method and a system for calculating a blocking effect index of a road network on a wild animal, which comprehensively consider blocking effect of a key road section and a key region on the wild animal, calculate blocking effect of the road section on the wild animal according to the road section, assign different weights to the road section, assign smaller weight to the road section with small blocking effect on the wild animal, and assign larger weight on the road section, so that the blocking effect index calculation result is more accurate.
3. The invention provides a method and a system for calculating a blocking effect index of a road network on wild animals, which comprehensively consider the habitat types of different wild animal types around important road sections and important areas, assign weights according to the habitat types, assign smaller habitat type weights to road sections with small blocking effect on the wild animals, and assign larger habitat type weights otherwise, and calculate the blocking effect index more accurately by adding the habitat type weights, so that the calculation accuracy is improved.
4. The invention provides a method and a system for calculating a barrier effect index of a road network on wild animals, which are based on a large amount of observation data, then carry out statistical analysis, carry out road avoidance distance difference analysis on different kinds of animals, and finally obtain a measuring and calculating distance which has more objectivity, thereby further improving the accuracy of a barrier effect index calculation result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for calculating a road network barrier effect index on wild animals according to embodiment 1 of the present invention;
FIG. 2 is a flowchart of determining the habitat type weights of different wild animal species under each road class in the road network to wild animal barrier effect index calculation method according to the embodiment 1 of the present invention;
FIG. 3 is a block diagram of a road network system for calculating the barrier effect index of wild animals according to embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the invention, are within the scope of the invention.
In the case of example 1,
Fig. 1 is a flowchart of a method for calculating a barrier effect index of a road network to a wild animal according to embodiment 1 of the present invention. Referring to fig. 1, the method specifically comprises the following steps:
Step S1, obtaining road information in a living area of a wild animal, wherein the road information comprises road grade and road weight.
Road network data in the living area of the wild animals are obtained through an open street map (OpenStreetMap, OSM), the road network data are composed of roads of various grades in the living area of the wild animals, and each grade road comprises a road name, a road number, a road type, a road length, whether the road is a bridge, whether the road is a tunnel or not and the like.
In the step S1, the road grades are classified according to road types, including: railway, highway, bridge, tunnel; in this embodiment, the evaluation range of the road weight is positioned to be 0-1, the closer to 0, the smaller the blocking effect of the road of the current grade on different wild animal types is, in particular, the tunnel has the smallest blocking influence degree on the wild animals due to the fact that the tunnel engineering slows down the fragmentation of the habitat of the wild animals, the road weight of the tunnel is evaluated to be 0, the railway has the largest blocking effect due to complete closure, and the road weight of the railway is evaluated to be 1.
In this embodiment, the roads in different road classes are classified according to road categories, including: expressways, primary highways, secondary highways, tertiary highways, and quaternary highways; the primary roads are classified according to road widths and comprise single primary roads and double primary roads; the secondary roads are classified according to the road frame, and comprise a single secondary road and a double secondary road; the three-level roads are classified according to road frame, and comprise a single-level three-level road and a double-level three-level road; the split roadbed is defined as a double-amplitude road, and the split roadbed and the integral roadbed which are both double-amplitude roads are temporarily not distinguished.
The expressway has the maximum complete sealing and blocking effects, and the road weight of the expressway is assigned to be 1; the side slopes on two sides of the separated roadbed need to occupy the land, the wild animals only need to pass through the roadbed once, the barrier effect of the double-width highway is larger than that of the single-width highway, and for this reason, the road weight is given to the primary highway, the secondary highway, the tertiary highway and the quaternary highway with the frames, specifically: the road weight of the single primary road is 0.8; the road weight of the double-amplitude primary road is 1; the road weight of the single-amplitude secondary road is 0.5; the road weight of the double-amplitude secondary road is 0.6; the road weight of the single three-level highway is 0.3; the road weight of the double-amplitude three-level road is 0.4; the road weight of the four-level highway is 0.25.
In this embodiment, the bridges in the road class are classified according to spans, including: medium-small bridges, large bridges and extra-large bridges; the medium-small bridges are classified according to road widths, and comprise single-width medium-small bridges and double-width medium-small bridges; the bridges are classified according to road widths, and comprise single-width bridges and double-width bridges;
Because the porous span total length of the super bridge is larger than 1000, the method can meet the crossing of ungulates, has less influence on the blocking of wild animals, and has the road weight of 0; for a bridge section of a double-amplitude separated roadbed, ungulates need to continuously pass through, the barrier effect is more obvious than that of a single amplitude, and for this reason, road weights are given to the bridges with the frames, and the road weights of the small and medium bridges in the single amplitude are 0.2; the road weight of the double-amplitude middle-small bridge is 0.25; the road weight of the single bridge is 0.1; the road weight of the double-amplitude bridge is 0.15.
Step S2, determining the total length of the road under each road grade according to the road information in step S1.
In this embodiment, the road length under each road level acquired by the open street map is the total road length.
And step S3, determining the habitat type weights of different wild animal types under each road level according to the road information in the step S1.
Fig. 2 is a flowchart of determining the habitat type weights of different wild animal species under each road class in the road network to wild animal barrier effect index calculation method provided in embodiment 1 of the present invention, and referring to fig. 2, the method specifically includes the following steps:
Step S31, obtaining habitat types of habitats where different wild animal species are located under each road grade;
The habitat types of different wild animal species under each road class comprise forests, grasslands, deserts and lakes;
The forests in the habitat type comprise a high coverage forest, a medium coverage forest and a low coverage forest; the high-coverage forest is thick in trees and free of bare lands, has rich biodiversity, has higher concealment for wild animal species, forms a relatively stable ecological system, has better concealment for wild animal species, is thinner in medium-coverage forest trees, is thinner in ground and is covered by some herbaceous plants, shrubs and moss, can provide certain concealment and protection, can provide certain trees and vegetation coverage, can hide bodies or avoid natural enemies in the ground, has little trees in low-coverage forest, means that most of the ground in the forest is bare, has poor concealment for wild animals due to the little trees, and lacks sufficient vegetation, means that animals are difficult to find a hidden body or are difficult to effectively avoid when facing predators or other threats;
Grasslands in the habitat type have fewer trees, are generally lower in concealment for wild animal species than forests, and have a wider field of view due to the lack of trees and other tall vegetation structures, which makes animals more easily found by natural enemies or predators when moving on the grasslands;
The desert in the habitat type is extremely low in concealment to wild animal species due to the special natural environment, and the surface of the desert is relatively wide due to the lack of trees and other shields, so that animals are easy to expose when moving in the desert;
step S32, determining vegetation coverage degree of habitat of different wild animal species under each road level according to habitat types of habitat of different wild animal species under each road level;
The vegetation coverage degree can also refer to vegetation coverage, is used for measuring an important index of the surface vegetation condition, is used for describing the ecological system condition and is also an important indication of the regional ecological system environment change, and in the embodiment, the vegetation coverage degree is expressed by adopting vegetation coverage, specifically refers to the ratio of the vegetation area to the total land area, and expressed by adopting percentage, the vegetation area generally refers to the shrub area, the farmland forest tree area and the coverage area of the surrounding trees;
The high-level covered forest is thick in trees and has higher concealment to wild animal species, the vegetation coverage in the high-level covered forest is higher, the vegetation coverage of the high-level covered forest is set to 90%, the ground of the medium-level covered forest is covered by luxuriant herbaceous plants, shrubs and moss, the concealment to the wild animal species is better, the vegetation coverage in the medium-level covered forest is described as belonging to medium-high level, the vegetation coverage of the high-level covered forest is slightly worse, the vegetation coverage of the medium-level covered forest is set to 70% -90%, the medium-level covered forest is sparse, the trees are sparse, the ground is covered by some herbaceous plants, shrubs and moss, a certain masking and protection can be provided, the vegetation coverage of the medium-level covered forest is set to 40% -70%, the low-level covered forest is almost free of trees, the concealment to the wild animal is worse, and the low-level covered forest is hardly found by the wild animal, and the vegetation coverage is set to 40% below the low-level covered by the embodiment;
The grasslands in the habitat type are less tree, the concealment of the wild animal species is generally low relative to forests, and the vegetation coverage of the grasslands is set to be below 20%;
In the desert in the habitat type, due to the lack of trees and other shields, the concealment to wild animals is extremely low, and the vegetation coverage rate of the desert is set below 5 percent;
The water system wild animals in the lakes in the habitat type survive in water without vegetation protection shielding, so that the vegetation coverage rate of the lakes is set to be 0 in the embodiment;
step S33, determining the weight of the habitat type of the different wild animal species under each road level according to the habitat type of the habitat of the different wild animal species under each road level and the vegetation coverage degree of the habitat of the different wild animal species under each road level;
The forest with higher vegetation coverage on both sides of the road under each road grade means that more plants and trees exist, which provides a certain shielding and protection for wild animals, the plants and trees can be used as habitats and refuges of the animals, so that the animals can safely move near the road, the barrier effect of the road on different wild animal species is smaller, and therefore, the embodiment sets the habitat type weight of the highly covered forest to 0.2; in the embodiment, the habitat type weight of the medium-height covered forest is set to be 0.5, the vegetation coverage rate of the two sides of the road under each road grade is extremely low, the blocking effect on different wild animals is large, the positions of the wild animals are easy to be exposed to threaten the wild animals, the habitat type weight of the low-height covered forest is set to be 0.8, the vegetation coverage rate of the medium-height covered forest is higher than that of the low-height covered forest, the blocking effect on the wild animals by the road is also smaller, and the habitat type weight of the medium-height covered forest is set to be 0.7;
Compared with the vegetation coverage of forests, the grasslands with lower vegetation coverage are positioned on the two sides of the road under each road grade, the concealment of wild animal species is lower, the wild animal positions are easy to be exposed, so that the wild animals are not dared to continue to advance towards the direction close to the road, the barrier effect of the road on the wild animals is larger, and the habitat type weight of the grasslands is set to be 0.8;
The desert with extremely low vegetation coverage, lacking trees and other shields, and extremely low concealment to wild animals compared with the vegetation coverage of forests and grasslands on both sides of the road under each road grade, so that the barrier effect of the road to the wild animals is extremely high, and the habitat type weight of the desert is set to be 1 in the embodiment; it is noted that camels are typical species in deserts, which can provide a navigation and path for camels, and camels have special physiological and behavioral characteristics and adapt to the desert environment, so that the camels have small or no barrier effect on migration in the deserts, and the wild animal species studied in this embodiment are species migration processes, such as large animal elephants, lions, tigers, etc., which are realized by changing routes through deserts in order to reduce the barrier effect of roads on them;
Lakes on two sides of each road are divided according to communication performance, including communication type lakes and closed type lakes, for closed type lakes, the living range of water system organisms is limited, and large-range migration and diffusion capacity cannot be realized, so that ecological system unbalance can be easily caused only by foraging in a specified range, the barrier effect is relatively large, vegetation coverage rate is not needed to be considered, the habitat type weight of the closed type lakes is set to be 1, and the habitat type weight of the connected type lakes is set to be 0.
Step S4, determining the distribution probability of different wild animal species in the road and the surrounding environment of the road under each road grade according to the road information in the step S1;
Step S41, determining environmental factor data of the road and the surrounding environment thereof and distribution point position data of different wild animals under each road grade according to the road information in the step S1;
The distribution point location data refer to the distribution quantity of different wild animals under different road grades in the grid area;
For the species distributed in a scattered manner, designing sample lines for a plurality of road grades, wherein the length of each sample line is 10km, recording the name, the number, the type and the geographical position information of the species by finding out species entities or traces on two sides of a road, preprocessing the obtained distribution number according to the preliminarily obtained wild animal distribution number, screening out the number of each wild animal species, selecting the wild animal number with more distribution number for researching the distribution probability, and improving the calculation accuracy;
The environmental factor data includes: precipitation, air temperature, solar radiation, altitude, slope direction, surface oxygen content, land coverage type, normalized vegetation index, night light data, and human footprint data; in consideration of excessive environmental factors, strong spatial correlation or complexity of a MaxEnt model, performing correlation analysis through SPSS software, removing environmental factors with larger correlation coefficients, and screening to obtain the following 9 environmental factor data, wherein the environmental factor data after screening comprises: precipitation, air temperature, solar radiation, altitude, gradient, slope direction, land coverage type, normalized vegetation index, human footprint data, used for construction of species distribution models;
Step S42, inputting the road grade, the environmental factor data and the distribution point location data of different wild animal species into a species distribution model, and obtaining the distribution probability of different wild animal species in the road under each road grade;
Constructing a species classification model, wherein the species classification model is a MaxEnt model and is used for analyzing the habitat suitability distribution conditions of different wild animal species; inputting the screened environmental factor data and distribution point location data into a trained MaxEnt model for prediction to obtain proper habitat distribution data of wild animal species, wherein the prediction result of the MaxEnt model is represented by a probability value of 0-1, namely the existing probability value is used for representing the suitability of the species distributed in the distribution region, wherein 0 is not suitable, 1 is very suitable, the predicted final result is imported into an ArcGIS for adaptive grading and visual expression, habitat is graded into four grades, grading is performed according to the suitability index of the habitat by using a grading method, namely 0.00-0.10 is an uncomfortable region, 0.10-0.30 is a low-suitable region, 0.30-0.50 is an edge suitable region, 0.50-0.70 is an adaptive region, and 0.70-1.00 is an optimal region.
Step S5, determining the avoidance distances of different wild animal species under each road grade according to the road information in the step S1; the avoidance distance refers to the blocking distance of different wild animal species under each road class.
The avoidance distances of different wild animal species under each road class are determined according to the road information, and the method specifically comprises the following steps:
Step S51, when species entities or species traces are found on two sides of different roads under each road class, recording species names, species numbers and species types;
step S52, measuring the vertical distance between different wild animal species and the road under each road grade;
And step S53, screening out the maximum vertical distance and the minimum vertical distance between different wild animals and the road in the step S52, and calculating an average value through the maximum vertical distance and the minimum vertical distance to determine the avoidance distance of different wild animals on the road under each road grade.
Because the probability distribution of the two sides of the road under each road level is uneven and sparse, the avoidance distances of the same species to different roads are temporarily not distinguished, the calculation result of the blocking effect index is prevented from being influenced, and the avoidance distances of the same wild animal species to the road under each road level are uniformly assigned to be 500m and 200m.
Step S6, calculating a road blocking effect index according to the road information, the total length of the road under each road grade, the habitat type weight of different wild animal species under each road grade, the avoidance distance of different wild animal species under each road grade and the distribution probability of different wild animal species in the road under each road grade and the surrounding environment thereof;
the calculation formula of the road blocking effect index is as follows:
；
Wherein: is road barrier effect index, n is road grade in wild animal living area,/> Road weight for road class i in wild animal living area,/>Is the total length of the road with the road class i in the living area of the wild animals,/>Is the avoidance distance of different wild animal species under the road with the road class i in the wild animal living area, and M is the wild animal living area,/>Is the distribution probability of different wild animal species in the road with road class i in the living area of the wild animal and the surrounding environment thereof,/>The weight value of the habitat type of different wild animal species under the road with the road class i in the living area of the wild animal.
When the barrier effect index of each road class is calculated, the embodiment takes the avoidance distance of the wild animals 3 times as the barrier influence domain at two sides of the road, so that the barrier effect of the road on the wild animals can be better quantified, and the road barrier effect index is calculated more accurately. In this embodiment, the final road blocking index is calculated by selecting six parameters including road class n, road weight a, road total length L, avoidance distance D, habitat type weight Q, and species distribution probability P.
Reasons for the parameter selection in the present embodiment:
A. Based on the road grade a and the road length L, when the road network is denser, the road grade is higher, the cutting degree of the region where the road network is located is stronger, and the cutting degree of wild animals is more serious, which indicates that the road construction has more obvious active blocking effects on migration, propagation, population communication and the like of the wild animals in the region;
B. Based on the avoidance distance D of the wild animals to the road, under the conditions that the cutting degree of the road to the area and the road grade are the same or similar, the avoidance distances of the wild animals of different types to the road are different, and the larger the avoidance distance is, the larger the barrier influence areas of the two sides of the road to the wild animals are;
C. Based on the distribution probability P of the wild animal species, the more the wild animals are, the more the distribution density is, the more the road influence degree is, which shows that the barrier effect of the road on the animals is stronger; when considering species distribution, the blocking effect of the same road grade is different in species distribution concentrated areas and non-concentrated areas with different distribution probabilities; in an exemplary case, when only the road grade, the road weight, the road total length, and the avoidance distance are considered, the actual barrier effect index will generate a road barrier effect index with a larger value difference due to the distribution probability of the species distribution area and the non-species distribution area after the species distribution factor is added under the condition that the two barrier effect indexes are the same, so the distribution probability of the wild animal species is also an important factor to be considered in the calculation of the road barrier effect index;
D. based on the habitat type weight Q of the wild animal species, the smaller the habitat type weight is, the higher the level of the habitat type is, the higher the concealment degree of the wild animal species is, the more suitable for the survival of the wild animal is, and the higher the concealment degree is, the smaller the barrier influence effect of the road on the habitat type is.
In the case of example 2,
FIG. 3 is a block diagram of a road network system for calculating the barrier effect index of wild animals according to embodiment 2 of the present invention. Referring to fig. 3, the invention further provides a road network barrier effect index calculation system for wild animals, which is used for executing the road network barrier effect index calculation method for wild animals, and comprises the following modules:
The road information acquisition module: the method comprises the steps of acquiring road grades and road weights in all grid areas in the living range of wild animals;
A first acquisition unit: the road information acquisition module is connected with the road information acquisition module and is used for acquiring the total length of the road under each road grade;
A second acquisition unit: the system comprises a road information acquisition module, a border information acquisition module and a border information processing module, wherein the road information acquisition module is connected with the road information acquisition module and is used for acquiring the weights of the types of habitats of different wild animal types under each road grade;
the third acquisition unit is connected with the road information acquisition module and is used for acquiring the distribution probability of different wild animal types in the road and the surrounding environment of the road under each road grade;
the fourth acquisition module is connected with the road information acquisition module and is used for acquiring avoidance distances of different wild animal types under each road grade;
road barrier effect index output module: and the method is used for outputting the calculation result of the road blocking effect index.
In the case of example 3,
A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of embodiments 1-2.
Any combination of one or more computer readable media may be employed in the present invention. The medium may be a computer readable signal medium or a computer readable storage medium. The medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application. As used in this specification, the terms "a," "an," "the," and/or "the" are not intended to be limiting, but rather are to be construed as covering the singular and the plural, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method or apparatus that includes the element.
It should also be noted that the positional or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the positional or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims (4)
1. The method for calculating the barrier effect index of the road network on the wild animals is characterized by comprising the following steps of:
Step S1, obtaining road information in a living area of a wild animal, wherein the road information comprises road grade and road weight;
Step S2, determining the total length of the road under each road grade according to the road information in the step S1;
step S3, determining the habitat type weights of different wild animal types under each road grade according to the road information in the step S1;
In the step S3, the habitat type weights of different wild animal species in each road class are determined according to the road information in the step S1, and the method specifically includes the following steps:
Step S31, obtaining habitat types of habitats where different wild animal species are located under each road grade;
The habitat types comprise forests, grasslands, deserts and lakes; the forests in the habitat type are divided according to vegetation coverage degree, and the forests comprise high coverage forests, medium coverage forests and low coverage forests;
step S32, determining vegetation coverage degree of habitat of different wild animal species under each road level according to habitat types of habitat of different wild animal species under each road level;
step S33, determining the weight of the habitat type of the different wild animal species under each road level according to the habitat type of the habitat of the different wild animal species under each road level and the vegetation coverage degree of the habitat of the different wild animal species under each road level;
The habitat type weight of the high coverage forest is 0.2, the habitat type weight of the medium coverage forest is 0.5, the habitat type weight of the medium coverage forest is 0.7, and the habitat type weight of the low coverage forest is 0.8;
the ecological type weight of the habitat type is 0.8;
The lakes in the habitat type are divided according to connectivity and comprise a connected lake and a closed lake, the habitat type weight of the connected lake is 0, and the habitat type weight of the closed lake is 1; the habitat type weight of the desert is 1;
step S4, determining the distribution probability of different wild animal species in the road and the surrounding environment of the road under each road grade according to the road information in the step S1;
step S5, determining the avoidance distances of different wild animal species under each road grade according to the road information in the step S1;
Step S6, calculating a road blocking effect index according to the road information, the total length of the road under each road grade, the habitat type weight of different wild animal species under each road grade, the avoidance distance of different wild animal species under each road grade and the distribution probability of different wild animal species in the road under each road grade and the surrounding environment thereof;
the calculation formula of the road blocking effect index is as follows:
；
Wherein: is road barrier effect index, n is road grade in wild animal living area,/> Road weight for road class i in wild animal living area,/>Is the total length of the road with the road class i in the living area of the wild animals,Is the avoidance distance of different wild animal species under the road with the road class i in the wild animal living area, and M is the wild animal living area,/>Is the distribution probability of different wild animal species in the road with road class i in the living area of the wild animal and the surrounding environment thereof,/>The weight value of the habitat type of different wild animal species under the road with the road class i in the living area of the wild animal.
2. The method for calculating the barrier effect index of road network to wild animals according to claim 1, wherein the step S4 is to determine the distribution probability of different wild animal species in the lower road and the surrounding environment according to the road information in the step S1, and specifically comprises the following steps:
Step S41, determining environmental factor data of the road and the surrounding environment thereof under each road grade and distribution point location data of different wild animal types according to the road information in the step S1;
And step S42, inputting the road grade, the environmental factor data and the distribution point location data of different wild animal species into a species distribution model, and obtaining the distribution probability of different wild animal species in the road under each road grade.
3. The method for calculating the barrier effect index of road network to wild animals according to claim 1, wherein the avoidance distance is the barrier distance of different wild animal species under each road class;
The step S5 is to determine the avoidance distances of different wild animal species under each road class according to the road information, and specifically comprises the following steps:
Step S51, when species entities or species traces are found on two sides of different roads under each road class, recording species names, species numbers and species types;
step S52, measuring the vertical distance between different wild animal species and the road under each road grade;
And step S53, screening out the maximum vertical distance and the minimum vertical distance between different wild animal species and the road in the step S52, and determining the avoidance distance of different wild animal species on the road in each road level by calculating the average value of the maximum vertical distance and the minimum vertical distance.
4. A road network barrier effect index calculation system for wild animals, for performing the road network barrier effect index calculation method for wild animals according to any one of claims 1-3, characterized by comprising the following modules:
The road information acquisition module: the method is used for acquiring all road grades and road weights within the living range of the wild animals;
A first acquisition unit: the road information acquisition module is connected with the road information acquisition module and is used for acquiring the total length of the road under each road grade;
A second acquisition unit: the system comprises a road information acquisition module, a border information acquisition module and a border information processing module, wherein the road information acquisition module is connected with the road information acquisition module and is used for acquiring the weights of the types of habitats of different wild animal types under each road grade;
the third acquisition unit is connected with the road information acquisition module and is used for acquiring the distribution probability of different wild animal types in the road and the surrounding environment of the road under each road grade;
the fourth acquisition module is connected with the road information acquisition module and is used for acquiring avoidance distances of different wild animal types under each road grade;
road barrier effect index output module: and the method is used for outputting the calculation result of the road blocking effect index.
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