CN-122023391-A - Road network ecological bearing capacity estimation method and system based on multi-source remote sensing data

CN122023391ACN 122023391 ACN122023391 ACN 122023391ACN-122023391-A

Abstract

The invention provides a road network ecological bearing capacity estimation method and system based on multisource remote sensing data, and relates to the technical fields of remote sensing technology and ecological assessment. The method comprises the steps of obtaining multispectral remote sensing images and basic data of an ecological system, preprocessing, performing supervision and classification on the remote sensing images, extracting linear ground object image spots, constructing topological relations, obtaining road network topological structure data, correlating historical traffic flow statistical data with road network space, interpolating to generate day and night noise space-time distribution data, extracting habitat distribution data based on a habitat type thematic map and species historical field investigation points, acquiring species acoustic sensitivity threshold data by combining behavior experiment records, superposing the noise space-time distribution data with the habitat distribution data, screening out noise data in a habitat range, comparing the noise data with the acoustic sensitivity threshold pixel by pixel, and counting the total area of pixels exceeding the threshold as road network ecological interference indexes, so that accurate estimation of road network ecological interference is achieved.

Inventors

LIU YUAN
JIN HUIHU
LI AIJING
DENG JINGCHENG
LI MEILING
YAO BING
FENG ZHIQIANG
GE LIYAN
XU GANG
WANG ZHIMING
XIONG HONGXIA

Assignees

交通运输部天津水运工程科学研究所

Dates

Publication Date: 20260512
Application Date: 20260407

Claims (9)

1. A road network ecological bearing capacity estimation method based on multi-source remote sensing data is characterized by comprising the following steps: S1, acquiring a multispectral remote sensing image of a monitoring area and ecosystem basic data of the monitoring area, performing geometric correction and radiation calibration on the multispectral remote sensing image to obtain a preprocessed remote sensing image, and formatting the ecosystem basic data to obtain a standardized basic data set; S2, performing supervision classification on road network elements on the preprocessed remote sensing image, extracting linear ground feature image spots in the image, and constructing topological relations of the linear ground feature image spots to obtain road network topological structure data of a monitoring area; S3, acquiring historical traffic flow statistical data of each road section in road network topological structure data, performing spatial position correlation on the historical traffic flow statistical data and each road section, and performing interpolation calculation on the correlated data according to a preset time slice to generate noise space-time distribution data of the road network in a day-night time dimension; S4, acquiring a habitat type thematic map of a monitoring area and historical field investigation point position data of a target species in the monitoring area, performing spatial superposition analysis on the historical field investigation point position data and the habitat type thematic map, extracting a habitat space range of the target species to obtain habitat distribution data of the target species, simultaneously acquiring behavior experiment records of the target species under different frequency noise stimulation, extracting critical noise intensity values of the target species when avoiding behaviors are generated from the behavior experiment records, and obtaining acoustic sensitivity threshold value data of the target species; And S5, spatially superposing the noise space-time distribution data and the habitat distribution data of the target species, screening out noise intensity data positioned in the range of the habitat distribution data, comparing the screened noise intensity data with the acoustic sensitivity threshold data of the target species pixel by pixel, and counting the total pixel area of the noise intensity data exceeding the acoustic sensitivity threshold data in the range of the habitat distribution data to obtain a road network ecological interference index for representing the activity interference degree of the road network on the target species.
2. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 1, wherein S2 comprises: S21, carrying out multi-scale segmentation on the preprocessed remote sensing image to generate a plurality of image objects composed of homogeneous pixels, and obtaining a candidate road network image spot object set; S22, performing geometric feature calculation on each object in the candidate road network pattern spot object set to obtain the length-width ratio, the shape index and the area parameter of each object, and obtaining the geometric shape feature set of the candidate pattern spot; S23, comparing the aspect ratio in the geometric feature set with a preset linear feature aspect ratio threshold, comparing the shape index with a preset road smoothness threshold, screening out objects meeting the linear feature aspect ratio threshold and the road smoothness threshold at the same time, and obtaining a target road network pattern spot object set; And S24, extracting a central line of an object in the target road network pattern object set, capturing an end point of the extracted central line, constructing a connection relation, and generating road network topological structure data consisting of nodes and arc segments.
3. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 2, wherein S22 comprises: s221, carrying out gradient calculation on each image object in the candidate road network pattern spot object set by adopting an edge detection operator, identifying edge points with step change of pixel values in the object, and connecting the edge points into closed boundary lines according to an eight-neighborhood tracking algorithm to obtain contour line data of each image object; S222, based on the contour line data of each image object, counting the total number of pixels contained in the area surrounded by the contour lines, and multiplying the total number of pixels by the actual ground area represented by a single pixel to obtain the area parameter of the image object; s223, based on the contour line data and the area parameter of each image object, measuring the total length of the contour line as a perimeter parameter, and taking the ratio of the square of the perimeter parameter to the area parameter as the shape index of the image object; S224, fitting an area surrounded by the contour line data by adopting a minimum circumscribed rectangle algorithm based on the contour line data of each image object, calculating a long side length value and a short side length value of a minimum rectangle completely containing the contour line, and taking the ratio of the long side length value to the short side length value as the aspect ratio of the image objects; And S225, carrying out attribute field association on the aspect ratio, the shape index and the area parameter, generating a feature record which corresponds to each image object and contains the aspect ratio, the shape index and the area parameter, and collecting the feature records of all the image objects to obtain a geometric feature set.
4. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 1, wherein S3 comprises: S31, acquiring historical traffic flow time sequence data of each road section issued by a traffic monitoring department, and carrying out classification statistics on the historical traffic flow time sequence data according to working days and rest days to obtain classified road section traffic flow basic data; S32, respectively accumulating and summing the classified road section traffic flow basic data according to the daytime period and the nighttime period to obtain a first average flow value of each road section in the daytime period and a second average flow value of each road section in the nighttime period; S33, acquiring digital elevation model data of a monitoring area, extracting a surface roughness grid chart from the digital elevation model data, taking the surface roughness grid chart as a surface attenuation factor of sound wave propagation, and simultaneously taking a first average flow value and a second average flow value as intensity parameters of a line sound source respectively, inputting the first average flow value and the second average flow value into a sound wave propagation geometric attenuation model for calculation, and generating a diurnal noise intensity grid chart and a night noise intensity grid chart; s34, carrying out band combination on the diurnal noise intensity grid diagram and the night noise intensity grid diagram to generate noise space-time distribution data containing diurnal and diurnal time dimensions.
5. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 1, wherein S4 comprises: S41, acquiring a satellite remote sensing image covering a monitoring area, and dividing land coverage types of the satellite remote sensing image by adopting an object-based image classification method to generate a habitat type thematic map containing forest, grassland, water area and farmland category information; S42, acquiring a space point position set recorded by global positioning system collar tracking of a target species in a wild animal monitoring database, and performing cleaning treatment for eliminating abnormal values on the space point position set to obtain historical field investigation point position data of the target species; S43, superposing the cleaned historical field investigation point position data on a habitat type thematic map in a vector point layer mode, extracting all habitat type map spots covered by the historical field investigation point position data, and merging to obtain habitat distribution data of target species; S44, obtaining hearing test data recorded by playback experiments on target species in a bioacoustic laboratory, analyzing hearing threshold values of the target species under pure tones with different frequencies from the hearing test data, determining the minimum value in the hearing threshold values as a critical noise intensity value of the target species for avoiding behaviors, and obtaining acoustic sensitivity threshold value data of the target species.
6. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 1, wherein S5 comprises: S51, carrying out space superposition on a grid layer where noise space-time distribution data are located and a vector layer where habitat distribution data of target species are located in geographic information system software, operating a mask extraction tool, extracting all noise pixels located in the boundary range of the habitat distribution data, and obtaining noise intensity grid data in the habitat; S52, carrying out logic operation on noise intensity data of each pixel in noise intensity grid data in the habitat and acoustic sensitivity threshold data of a target species, assigning 1 to pixels with noise intensity data larger than the acoustic sensitivity threshold data, and assigning 0 to the rest pixels to generate a noise interference Boolean grid image; and S53, counting the total number of pixels with the value of 1 in the noise interference Boolean grid diagram, multiplying the total number of pixels by the actual geographic area represented by a single pixel, calculating to obtain the noise interference area in the range of the habitat distribution data, and taking the noise interference area as the road network ecological interference index.
7. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 6, wherein S5 further comprises: S54, acquiring land utilization covering grid data of a monitoring area, carrying out resistance value assignment on the blocking degree of migration of a target species according to different land utilization types, and constructing species migration resistance surface grid data; S55, acquiring two plaques with the largest area in habitat distribution data, respectively serving as a migration source point and a migration target point, inputting the migration source point, the migration target point and species migration resistance surface grid data into a cost connectivity tool, running a shortest path algorithm, and simulating and generating theoretical potential ecological corridor vector line data; S56, converting the noise interference Boolean raster image into vector polygons to obtain noise interference area vector data, performing spatial superposition on the potential ecological corridor vector line data and the noise interference area vector data, cutting out a corridor section penetrated by the noise interference area vector data, measuring the total length of the penetrated corridor section to obtain corridor connectivity loss data, and taking the corridor connectivity loss data as a component part of a road network ecological interference index.
8. The road network ecological bearing capacity estimation method based on multi-source remote sensing data according to claim 7, wherein S54 comprises: S541, acquiring a global land coverage data product of a monitored area, and extracting land coverage pattern spots of cultivated land, construction land, woodland and water areas from the global land coverage data product; s542, according to the crossing preference of target species recorded in biological literature on different ground objects, a first resistance value is given to cultivated ground objects, a second resistance value is given to construction ground objects, a third resistance value is given to forest ground objects, a fourth resistance value is given to water area objects, wherein the second resistance value is larger than the first resistance value, the first resistance value is larger than the fourth resistance value, and the fourth resistance value is larger than the third resistance value; And S543, performing grid conversion and merging on the assigned various ground object type image spots to generate species migration resistance surface grid data which covers the full monitoring area and has resistance values for each pixel.
9. A road network ecological bearing capacity estimation system based on multi-source remote sensing data, which is characterized in that the system adopts the road network ecological bearing capacity estimation method based on the multi-source remote sensing data as set forth in any one of claims 1 to 8, and the system comprises: The data acquisition and preprocessing module is used for executing the step S1 of acquiring multispectral remote sensing images of a monitoring area and ecosystem basic data of the monitoring area, performing geometric correction and radiation calibration on the multispectral remote sensing images to obtain preprocessed remote sensing images, and formatting the ecosystem basic data to obtain a standardized basic data set; The road network topological structure extraction module is used for executing the step S2 of performing supervision classification of road network elements on the preprocessed remote sensing image, extracting linear ground feature image spots in the image, constructing topological relations of the linear ground feature image spots and obtaining road network topological structure data of a monitoring area; The noise space-time distribution simulation module is used for executing the step S3 of acquiring historical traffic flow statistical data of each road section in road network topological structure data, carrying out spatial position correlation on the historical traffic flow statistical data and each road section, carrying out interpolation calculation on the correlated data according to a preset time slice, and generating noise space-time distribution data of the road network in the day-night time dimension; The species sensitive information acquisition module is used for executing the step S4 of acquiring a habitat type thematic map of a monitoring area and historical field investigation point position data of a target species in the monitoring area, carrying out space superposition analysis on the historical field investigation point position data and the habitat type thematic map, extracting a habitat space range of the target species to obtain habitat distribution data of the target species, simultaneously acquiring behavior experiment records of the target species under different frequency noise stimulus, extracting critical noise intensity values of the target species when avoiding behaviors are generated from the behavior experiment records, and obtaining acoustic sensitivity threshold value data of the target species; And S5, carrying out spatial superposition on the noise space-time distribution data and the habitat distribution data of the target species, screening out noise intensity data in the range of the habitat distribution data, carrying out pixel-by-pixel comparison on the screened noise intensity data and the acoustic sensitivity threshold data of the target species, and counting the total area of pixels of the noise intensity data exceeding the acoustic sensitivity threshold data in the range of the habitat distribution data to obtain the road network ecological interference index for representing the activity interference degree of the road network on the target species.

Description

Road network ecological bearing capacity estimation method and system based on multi-source remote sensing data Technical Field The invention relates to the technical field of remote sensing technology and ecological assessment, in particular to a road network ecological bearing capacity estimation method and system based on multi-source remote sensing data. Background Under the background of coordinated development of traffic infrastructure planning and ecological protection, the pressure generated by road network construction on regional ecological environment is increasingly concerned. The ecological bearing capacity is used as an index for measuring the interference bearing capacity of the regional ecological system on human activities, and the accurate estimation of the ecological bearing capacity is of great significance to the reasonable layout of road networks and the establishment of ecological relief measures. However, the existing ecological bearing capacity assessment method depends on statistical data and field investigation, and it is difficult to finely describe dynamic interference of road network operation on an ecological system on a large spatial scale. Particularly, for noise pollution generated by road networks, the time-space distribution is influenced by multiple factors such as traffic flow, topography and the like, and the traditional method lacks an efficient and accurate estimation means. In addition, the sensitivity degree of different species to noise is different, and the biological characteristic is often ignored in the existing evaluation system, and the unified noise threshold is adopted for evaluation, so that deviation exists between an evaluation result and an ecological actual influence. Therefore, how to comprehensively utilize multi-source data and realize high-precision and targeted estimation of the ecological bearing capacity of the road network is a technical problem to be solved in the field. Disclosure of Invention In order to solve the above problems in the prior art, a first aspect of the present invention provides a road network ecological bearing capacity estimation method based on multi-source remote sensing data, including: S1, acquiring a multispectral remote sensing image of a monitoring area and ecosystem basic data of the monitoring area, performing geometric correction and radiation calibration on the multispectral remote sensing image to obtain a preprocessed remote sensing image, and formatting the ecosystem basic data to obtain a standardized basic data set; S2, performing supervision classification on road network elements on the preprocessed remote sensing image, extracting linear ground feature image spots in the image, and constructing topological relations of the linear ground feature image spots to obtain road network topological structure data of a monitoring area; S3, acquiring historical traffic flow statistical data of each road section in road network topological structure data, performing spatial position correlation on the historical traffic flow statistical data and each road section, and performing interpolation calculation on the correlated data according to a preset time slice to generate noise space-time distribution data of the road network in a day-night time dimension; S4, acquiring a habitat type thematic map of a monitoring area and historical field investigation point position data of a target species in the monitoring area, performing spatial superposition analysis on the historical field investigation point position data and the habitat type thematic map, extracting a habitat space range of the target species to obtain habitat distribution data of the target species, simultaneously acquiring behavior experiment records of the target species under different frequency noise stimulation, extracting critical noise intensity values of the target species when avoiding behaviors are generated from the behavior experiment records, and obtaining acoustic sensitivity threshold value data of the target species; And S5, spatially superposing the noise space-time distribution data and the habitat distribution data of the target species, screening out noise intensity data positioned in the range of the habitat distribution data, comparing the screened noise intensity data with the acoustic sensitivity threshold data of the target species pixel by pixel, and counting the total pixel area of the noise intensity data exceeding the acoustic sensitivity threshold data in the range of the habitat distribution data to obtain a road network ecological interference index for representing the activity interference degree of the road network on the target species. In a second aspect, the present invention provides a road network ecological bearing capacity estimation system based on multi-source remote sensing data, where the system adopts the road network ecological bearing capacity estimation method based on multi-source remote sensing data provi