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CN-122022040-A - Ecological protection priority zone identification method and system based on multi-source data and space optimization

CN122022040ACN 122022040 ACN122022040 ACN 122022040ACN-122022040-A

Abstract

The invention relates to an ecological protection priority area identification method and system based on multi-source data and space optimization, wherein the method comprises the steps of collecting multi-source space data in a target area and preprocessing to generate a theme space data set; the method comprises the steps of quantifying ecosystem services in a target area to obtain a standardized magnitude space distribution map, combining species distribution points in the target area with a theme space data set, simulating and generating a suitability space distribution map, fusing the standardized magnitude space distribution map and the suitability space distribution map to form an area comprehensive protection value curved surface, carrying out iterative computation on the area comprehensive protection value curved surface by adopting a space optimization algorithm to obtain an ecological protection space layout scheme, and demarcating protection level priority ecological protection areas of different protection levels of the target area. The invention greatly improves the accuracy, scientificity and operability of the identification demarcation process of the ecological protection priority area, and provides a high-efficiency technical approach for the collaborative protection of the biodiversity and the ecological system service.

Inventors

  • LI WEI
  • LV SISI
  • ZHAO WEIQUAN
  • ZHAO ZULUN

Assignees

  • 贵州省山地资源研究所

Dates

Publication Date
20260512
Application Date
20260130

Claims (10)

  1. 1. An ecological protection priority zone identification method based on multi-source data and space optimization is characterized by comprising the following steps: the method comprises the steps of collecting multi-source space data in a target area, wherein the multi-source space data comprise remote sensing data, topographic data, meteorological data, soil data, species distribution data, basic geographic information data and socioeconomic data; Preprocessing the multi-source space data to generate a theme space data set; based on the theme space dataset, quantifying the ecosystem services in the target area by adopting a InVEST model, and acquiring a standardized magnitude space distribution diagram of each ecosystem service in the target area; Combining species distribution points in the target area with the subject space data set, simulating the space suitability of the regional biodiversity based on a MaxEnt ecological niche species distribution model, and generating a suitability space distribution map of the biodiversity of the target area; fusing the normalized magnitude spatial distribution diagram and the suitability spatial distribution diagram to form a regional comprehensive protection value curved surface; Carrying out iterative computation on the curved surface of the comprehensive protection value of the region by adopting a space optimization algorithm to obtain an ecological protection space layout scheme with preset protection efficiency; And defining different protection level priority ecological protection areas of the target area based on the ecological protection space layout scheme.
  2. 2. The method for identifying an ecological protection priority zone based on multi-source data and space optimization according to claim 1, wherein preprocessing the multi-source space data comprises: and performing spatial projection unification, data noise removal, raster data spatial resolution resampling and spatial interpolation operation of meteorological elements on the multi-source spatial data.
  3. 3. The method for identifying an ecological protection priority zone based on multi-source data and spatial optimization according to claim 2, wherein the spatial interpolation operation of meteorological elements comprises: And calculating the annual total precipitation and the annual potential evapotranspiration of each observation station according to the observation data of the target area and a plurality of meteorological observation stations around the target area, wherein the annual potential evapotranspiration is calculated by adopting a Pengman-Meng Tesi formula, the interpolation method selects a digital elevation model of the target area as a covariate, and the annual precipitation and the annual potential evapotranspiration calculated by the stations are spatially interpolated by adopting a Aunsplin interpolation method.
  4. 4. The method for identifying an ecological protection priority zone based on multi-source data and space optimization according to claim 1, wherein the ecosystem service comprises: the soil maintenance service comprises the steps of carrying out evaluation and quantification by adopting a corrected general soil loss equation RUSLE, and obtaining a quantized soil maintenance service layer of a target area; The water conservation service is characterized in that the unit water yield is represented by the difference value of annual rainfall and annual evaporation in the unit based on Budyko hydrothermal coupling balance hypothesis theory, and the part effectively intercepted by an ecological system in the water yield is the water conservation quantity, so that the spatial distribution pattern of the water conservation service of a target area is obtained; estimating based on an ecosystem carbon library superposition summation method, and obtaining a spatial distribution diagram of the carbon storage service; The habitat quality service is based on superposition of habitat suitability and threat factors, and estimation is carried out by combining with an exponential decay equation of a habitat threat source, so that a spatial distribution diagram of the habitat quality service is generated; The water quality purifying service is carried out based on the nutrient retention rate module to evaluate the water quality purifying service and generate a spatial distribution diagram of the water quality purifying service; Grain supply service, accounting grain supply service based on crop yield and farmland distribution method to obtain spatial supply service distribution map.
  5. 5. The method for identifying an ecological protection priority region based on multi-source data and space optimization according to claim 1, wherein simulating the space suitability of regional biodiversity based on a MaxEnt niche species distribution model comprises: screening environment variables, wherein the environment variables comprise target area gradient, slope direction terrain factors, land utilization, vegetation indexes, soil types and soil textures; Checking the environment variable; And inputting the checked variables into a MaxEnt ecological niche species distribution model for simulation.
  6. 6. The method for identifying an ecological protection priority zone based on multi-source data and spatial optimization as recited in claim 5, wherein verifying the environmental variable comprises: And checking the environment variables by adopting a multiple collinearity checking method, removing variables with variance expansion factors larger than a set value from the variables, and using variables with variance expansion factors smaller than the set value as input parameters of the model.
  7. 7. The method for identifying an ecological protection priority zone based on multi-source data and spatial optimization according to claim 1, wherein fusing the normalized magnitude spatial profile with the suitability spatial profile comprises: Converting each index into a non-dimensional layer with a value range of 0-1 by adopting a range normalization method; Acquiring the comprehensive weight of each index by adopting a comprehensive weighting method to form a comprehensive weight system; And multiplying each normalized ecological index layer by the corresponding weight, and carrying out space weighted superposition on the ecological index layers to obtain the regional comprehensive protection value curved surface.
  8. 8. The method for identifying an ecological protection priority zone based on multi-source data and space optimization according to claim 7, wherein the step of obtaining the comprehensive weight of each index by adopting the comprehensive weighting method comprises the steps of: establishing a judgment matrix based on expert consultation, and obtaining subjective weights of all indexes by using a analytic hierarchy process; According to the difference of the discrete degree of each index data, calculating the objective weight of each index by using an entropy weight method; And calculating the comprehensive weight of each index by adopting the subjective weight and the objective weight.
  9. 9. The method for identifying an ecological protection priority area based on multi-source data and space optimization according to claim 1, wherein the iterative computation is performed on the regional comprehensive protection value curved surface by adopting a space optimization algorithm, and the ecological protection space layout scheme for obtaining the preset protection efficiency comprises the following steps: Inputting the characteristics of the region comprehensive protection value curved surface serving as a core into a layer, and inputting the characteristics into the space optimization algorithm; in the iterative calculation process, setting a boundary length penalty coefficient as a fixed value, and solving by using an addition type boundary loss function; The output result is a reserve priority score for each spatial unit.
  10. 10. An ecological protection priority zone identification system based on multi-source data and space optimization, characterized in that it is used to implement the method according to any one of claims 1-9, said system comprising: The system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring multi-source space data in a target area, and the multi-source space data comprise remote sensing data, topographic data, meteorological data, soil data, species distribution data, basic geographic information data and socioeconomic data; the data preprocessing module is used for preprocessing the multi-source space data to generate a theme space data set; The data quantization module is used for quantizing the ecosystem services in the target area by adopting a InVEST model based on the theme space dataset and obtaining a standardized magnitude space distribution diagram of each ecosystem service in the target area; The data simulation module is used for combining species distribution points in the target area with the subject space data set, simulating the space suitability of the area biodiversity based on the MaxEnt ecological locus species distribution model, and generating a suitability space distribution map of the target area biodiversity; The data fusion module is used for fusing the normalized magnitude spatial distribution diagram and the suitability spatial distribution diagram to form a regional comprehensive protection value curved surface; The iterative computation module is used for carrying out iterative computation on the regional comprehensive protection value curved surface by adopting a space optimization algorithm to obtain an ecological protection space layout scheme with preset protection efficiency; and the ecological protection area demarcation module is used for demarcating the priority ecological protection areas of different protection levels of the target area based on the ecological protection space layout scheme.

Description

Ecological protection priority zone identification method and system based on multi-source data and space optimization Technical Field The invention relates to the technical field of cross application of geospatial information technology and landscape ecology, in particular to an ecological protection priority zone identification method and system based on multi-source data and space optimization. Background The influence of globalization process is aggravated and the human activity is continuously enhanced in recent years, how to scientifically define the regional ecological protection priority region becomes one of important preconditions for guaranteeing regional ecological safety and promoting regional sustainable development, however, most of current protection region planning mainly uses the experience of a certain class of ecological elements or related expert scholars, the scientificity and the systemicity are obviously insufficient, and the problems of lack of spatial explicit expression, single-target optimization and the like are more outstanding. At present, with the necessary results brought by the space analysis technology and the multi-source heterogeneous space geographic data accumulation on the basis of big data, the multi-dimensional ecological characteristics are fused, the recognition of the space priority is possible by scientifically applying an optimization algorithm, but how to coordinate different multi-source heterogeneous data, couple different models and realize the collaborative optimization among space optimization targets is still required to be perfected continuously. Disclosure of Invention The invention provides an ecological protection priority zone identification method and system based on multi-source data and space optimization, which are used for solving the technical defects of poor systematicness, low operability, low space dominance expression degree, excessive dependence on related subjective experience and the like in regional ecological priority zone planning. In order to achieve the above object, the present invention provides the following solutions: an ecological protection priority zone identification method based on multi-source data and space optimization comprises the following steps: the method comprises the steps of collecting multi-source space data in a target area, wherein the multi-source space data comprise remote sensing data, topographic data, meteorological data, soil data, species distribution data, basic geographic information data and socioeconomic data; Preprocessing the multi-source space data to generate a theme space data set; based on the theme space dataset, quantifying the ecosystem services in the target area by adopting a InVEST model, and acquiring a standardized magnitude space distribution diagram of each ecosystem service in the target area; Combining species distribution points in the target area with the subject space data set, simulating the space suitability of the regional biodiversity based on a MaxEnt ecological niche species distribution model, and generating a suitability space distribution map of the biodiversity of the target area; fusing the normalized magnitude spatial distribution diagram and the suitability spatial distribution diagram to form a regional comprehensive protection value curved surface; Carrying out iterative computation on the curved surface of the comprehensive protection value of the region by adopting a space optimization algorithm to obtain an ecological protection space layout scheme with preset protection efficiency; And defining different protection level priority ecological protection areas of the target area based on the ecological protection space layout scheme. Optionally, preprocessing the multi-source spatial data includes: and performing spatial projection unification, data noise removal, raster data spatial resolution resampling and spatial interpolation operation of meteorological elements on the multi-source spatial data. Optionally, the spatial interpolation operation of the meteorological element includes: And calculating the annual total precipitation and the annual potential evapotranspiration of each observation station according to the observation data of the target area and a plurality of meteorological observation stations around the target area, wherein the annual potential evapotranspiration is calculated by adopting a Pengman-Meng Tesi formula, the interpolation method selects a digital elevation model of the target area as a covariate, and the annual precipitation and the annual potential evapotranspiration calculated by the stations are spatially interpolated by adopting a Aunsplin interpolation method. Optionally, the ecosystem service includes: the soil maintenance service comprises the steps of carrying out evaluation and quantification by adopting a corrected general soil loss equation RUSLE, and obtaining a quantized soil maintenance service layer of a target area;