CN-121980295-A - Ecological restoration area intelligent identification method based on multi-source data and model integration

CN121980295ACN 121980295 ACN121980295 ACN 121980295ACN-121980295-A

Abstract

The application relates to an intelligent ecological restoration area identification method based on multi-source data and model integration. The method comprises the steps of obtaining multisource remote sensing images, terrains and soil attribute data, generating multisource data substrates and ecological source land sets through feature extraction, constructing an ecological resistance surface based on the multisource data substrates, conducting circuit theory simulation based on the ecological resistance surface and the ecological source land sets, generating a reference current density distribution map and a reference equivalent connectivity index, virtually repairing the multisource data substrates based on the ecological resistance surface and the ecological resistance surface, generating a communication gain distribution map, calculating gain cost ratio based on the communication gain distribution map, the terrain gradient and the road distance data, generating a priority repairing potential distribution map, and conducting space clustering analysis based on the priority repairing potential distribution map to generate an ecological repairing key area. By adopting the method, benefits and cost can be taken into account, and the ecological restoration key area can be accurately identified.

Inventors

LIU JIARU
ZHANG FUCUN
LIU YAN
YUAN DONGHAI
Yue Zhengfan
Sheng Shengfang
LI SHAOFANG
Wan Mazhuogai
ZHANG SIHAN

Assignees

青海理工学院

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (10)

1. An intelligent ecological restoration area identification method based on multi-source data and model integration is characterized by comprising the following steps: S1, acquiring a multi-source remote sensing image, topographic data and soil attribute data, and extracting characteristics of the multi-source remote sensing image, the topographic data and the soil attribute data to generate a multi-source data substrate and an ecological source land set; s2, constructing an ecological resistance surface according to the multi-source data substrate, and generating an ecological resistance surface; S3, performing circuit theory simulation operation according to the ecological resistance surface and the ecological source-ground set to generate a reference current density distribution diagram and a reference equivalent connectivity index; s4, performing virtual restoration simulation according to the ecological resistance surface and the multi-source data substrate to generate a communication gain distribution diagram; S5, calculating gain cost ratio according to the communication gain distribution diagram, the terrain gradient and the road distance data, and generating a priority repair potential distribution diagram; S6, performing spatial clustering analysis according to the preferential repair potential distribution diagram to generate an ecological repair key region.
2. The method according to claim 1, wherein S2 comprises: S21, extracting resistance parameters according to land utilization types, terrain factors, artificial interference intensity and vegetation coverage in the multi-source data substrate, and generating a basic resistance coefficient and a correction factor set; S22, performing multidimensional factor coupling operation according to the basic resistance coefficient and the correction factor set to generate a final ecological resistance value, wherein the calculation formula of the final ecological resistance value is as follows: Wherein, the Is the position The final ecological resistance value at the point, As a basic coefficient of resistance, As a function of the normalized gradient factor, For the normalized jamming strength factor, For the normalized vegetation coverage factor, 、、 The terrain sensitivity coefficient, the interference sensitivity coefficient and the vegetation coverage correction coefficient are respectively; S23, performing gridding mapping according to the final ecological resistance value to generate the ecological resistance surface.
3. The method according to claim 2, wherein said S3 comprises: s31, performing node mapping and conductivity matrix conversion of the ecological resistance surface according to the ecological source-ground set to generate a conductivity matrix; S32, solving a linear equation set according to the conductivity matrix to generate voltage distribution and current distribution; S33, extracting connectivity indexes according to the voltage distribution and the current distribution, and generating the reference current density distribution diagram and the reference equivalent connectivity indexes.
4. The method according to claim 1, wherein S4 comprises: s41, extracting barrier points according to the ecological resistance surface to generate a candidate unit set to be repaired; S42, updating the resistance value of the candidate unit set to be repaired according to a preset ideal recovery state attribute to generate a temporary ecological resistance surface; And S43, performing gain calculation according to the temporary ecological resistance surface and the reference equivalent connectivity index, and generating the communication gain distribution diagram.
5. The method of claim 4, wherein S43 comprises: S431, carrying out local voltage update according to the temporary ecological resistance surface to generate updated voltage distribution; s432, calculating an equivalent connectivity index after repair according to the updated voltage distribution, and generating an equivalent connectivity index after repair; S433, performing difference calculation according to the repaired equivalent connectivity index and the reference equivalent connectivity index to generate the communication gain distribution diagram.
6. The method according to claim 1, wherein S5 comprises: S51, carrying out implementation cost index calculation according to the terrain gradient data, the road distance data and the land weight data to generate implementation cost distribution, wherein a calculation formula of the implementation cost index is as follows: Wherein, the Is the position An implementation cost index at the point of sale, Is the gradient of the slope of the steel plate, For the maximum slope to be the largest, In order to be a distance from the road, For the maximum distance of the distance between the two, Is the weight factor of the land and is a factor of the land, 、、 The gradient weight coefficient, the distance weight coefficient and the weight coefficient are respectively; S52, performing gain cost ratio operation according to the implementation cost distribution and the communication gain distribution diagram to generate a gain cost ratio matrix, wherein the calculation formula of the gain cost ratio is as follows: Wherein, the Is the position The gain-to-cost ratio at which, In order to communicate the gain value(s), In order to implement the cost index of the present invention, 、 To adjust parameters; And S53, performing space mapping according to the gain cost ratio matrix to generate the preferential repair potential distribution map.
7. The method according to claim 1, wherein S6 comprises: S61, carrying out threshold screening according to the preferential repair potential distribution diagram to generate a target repair pixel set; S62, performing density clustering according to the target repair pixel set to generate a clustering patch set; And S63, carrying out priority ranking according to the average gain cost ratio of the clustering plaque set, and generating the ecological restoration key region.
8. An ecological restoration area intelligent identification device based on multisource data and model integration, which is characterized by comprising: The feature extraction module is used for acquiring multi-source remote sensing images, topographic data and soil attribute data, and extracting features of the multi-source remote sensing images, the topographic data and the soil attribute data to generate a multi-source data substrate and an ecological source set; The ecological resistance surface construction module is used for constructing an ecological resistance surface according to the multi-source data substrate and generating an ecological resistance surface; The circuit theory simulation operation module is used for carrying out circuit theory simulation operation according to the ecological resistance surface and the ecological source-ground set to generate a reference current density distribution diagram and a reference equivalent connectivity index; the virtual restoration simulation module is used for performing virtual restoration simulation according to the ecological resistance surface and the multi-source data substrate to generate a communication gain distribution diagram; the gain cost ratio calculation module is used for performing gain cost ratio calculation according to the communication gain distribution diagram, the terrain gradient and the road distance data to generate a priority repair potential distribution diagram; And the spatial cluster analysis module is used for carrying out spatial cluster analysis according to the preferential repair potential distribution diagram to generate an ecological repair key region.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.

Description

Ecological restoration area intelligent identification method based on multi-source data and model integration Technical Field The invention belongs to the technical field of intelligent recognition, and particularly relates to an intelligent recognition method for an ecological restoration area based on multi-source data and model integration. Background With the development of the technology of ecological restoration of the homeland space and landscape ecology, the construction of an ecological network and the identification of restoration key areas by using a Geographic Information System (GIS) and multi-source remote sensing data are mainstream technical means. The technical means can intuitively reflect the spatial distribution characteristics and dynamic changes of the regional landscape pattern, provides scientific and quantitative data support for the ecological restoration planning, and enables accurate identification and evaluation of the broken habitat to be possible. In the conventional technology, identifying an ecological restoration key area is mainly based on a minimum accumulated resistance Model (MCR) and a circuit theory for spatial analysis. Specifically, researchers generally construct an ecological resistance surface according to basic geographic elements such as land utilization types, topography, vegetation coverage and the like, extract ecological source lands through morphological spatial analysis, and simulate the motion trail of ecological flows in landscapes by utilizing circuit theory. On the basis, the area with higher current density is extracted as an ecological pinch point, or the area with connectivity interruption caused by high resistance is identified as an ecological barrier point, and the area with poor landscape connectivity or flow bottleneck under the current situation is directly determined as the area needing to be repaired preferentially. However, the current identification mode usually only focuses on resistance distribution and flow bottleneck under the current landscape pattern, and belongs to static diagnosis of the existing ecological problems. The method cannot quantify how much the communication performance of the whole ecological system can be improved after repairing a specific area, namely a mechanism for dynamically simulating and evaluating expected benefits brought by repairing behaviors is lacking. Meanwhile, in the prior art, the balance between engineering implementation difficulty and ecological restoration benefits is often ignored in the planning process, so that the screened restoration area may have the problems of overhigh input cost and limited connectivity improvement. Disclosure of Invention Based on the above, it is necessary to provide an intelligent recognition method for an ecological restoration area based on multi-source data and model integration, which can intelligently and accurately recognize the area with the most restoration value from the dual aspects of maximization of connectivity gain and engineering economic feasibility. In a first aspect, the application provides an intelligent ecological restoration area identification method based on multi-source data and model integration, which comprises the following steps: s1, acquiring a multi-source remote sensing image, topographic data and soil attribute data, and performing feature extraction on the multi-source remote sensing image, the topographic data and the soil attribute data to generate a multi-source data substrate and an ecological source land set; S2, constructing an ecological resistance surface according to the multisource data base, and generating the ecological resistance surface; S3, performing circuit theoretical simulation operation according to the ecological resistance surface and the ecological source and ground set to generate a reference current density distribution diagram and a reference equivalent connectivity index; S4, performing virtual restoration simulation according to the ecological resistance surface and the multi-source data substrate to generate a communication gain distribution diagram; s5, calculating gain cost ratio according to the communication gain distribution diagram, the terrain gradient and the road distance data, and generating a priority repair potential distribution diagram; and S6, performing spatial clustering analysis according to the preferential repair potential distribution diagram to generate an ecological repair key region. In one embodiment, S2 comprises: S21, extracting resistance parameters according to land utilization types, terrain factors, artificial interference intensity and vegetation coverage in the multi-source data substrate, and generating a basic resistance coefficient and a correction factor set; S22, performing multidimensional factor coupling operation according to the basic resistance coefficient and the correction factor set to generate a final ecological resistance value, wherein the calculation form