A method for quantitatively evaluating habitat quality of fish habitat in a basin based on a GIS spatially weighted synthesis model

Abstract

The application discloses a kind of based on GIS spatial weighting synthesis model's watershed fish habitat habitat quality quantitative evaluation method, it is related to watershed ecological environment evaluation, GIS spatial data analysis, ecological quality monitoring technical field, including the following steps: S1, watershed multi-source ecological data acquisition and pretreatment, collect the natural background data and human activity data of target watershed;S2, construct natural-human activity dual factor evaluation index system;S3, construct GIS spatial weighting comprehensive evaluation model;S4, watershed habitat quality macro quantitative calculation;S5, high intensity habitat interference area accurate identification;S6, global habitat evolution law mining, the application first realizes natural background inherent factor and human activity disturbance factor depth coupling, gives consideration to natural ecological evolution law and human development influence, completely solve the problem of traditional evaluation factor single, result is one-sided, and evaluation result is more close to watershed real ecological present situation.

Description

Technical Field

[0001] This invention relates to the fields of watershed ecological environment assessment, GIS spatial data analysis, and ecological quality monitoring, specifically a method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model. Background Technology

[0002] Watershed habitats are the core carriers of regional ecosystems. Their quality directly determines the level of ecological functions such as biodiversity maintenance, soil and water conservation, and water source conservation in the watershed. They are the core basis for watershed ecological civilization construction, ecological restoration and governance, and territorial spatial planning. With the rapid advancement of urbanization and industrialization, watersheds are increasingly affected by the combined effects of natural evolution and human development activities, resulting in increasingly prominent problems of habitat fragmentation and degradation. Accurately quantifying watershed habitat quality, identifying areas of high-intensity disturbance, and mastering the laws of habitat evolution are the prerequisites and foundations for carrying out refined watershed ecological governance.

[0003] Existing watershed habitat quality assessment technologies have many shortcomings: First, traditional assessment methods often rely solely on natural ecological indicators, ignoring the core impact of high-intensity human disturbance, failing to reflect the coupling effect of both natural and human factors, and resulting in a significant deviation between the assessment results and the actual ecological status. Secondly, conventional evaluation models mostly adopt a weighted average method across the entire region, failing to consider the spatial heterogeneity of the watershed, thus failing to achieve accurate assessment of local regional differences and making it difficult to identify small-scale areas of high-intensity disturbance. Thirdly, existing technologies mostly focus on static single-point assessments, lacking the mining of long-term, global evolutionary patterns, failing to accurately predict habitat degradation trends, and failing to support dynamic ecological management. Fourthly, traditional methods for identifying disturbance areas have single thresholds and low accuracy, failing to distinguish between habitat degradation caused by normal natural fluctuations and high-intensity human disturbance, resulting in insufficient accuracy in identifying disturbance areas.

[0004] To address the technical pain points of existing technologies, such as insufficient factor coupling, low spatial accuracy, inadequate pattern mining, and ambiguous interference identification, there is an urgent need to develop a watershed habitat quality assessment method that takes into account both natural background and human activities, possesses spatial differentiation assessment capabilities, and can achieve full-domain pattern mining and accurate interference identification. Summary of the Invention

[0005] This invention provides a quantitative assessment method for the habitat quality of fish habitats in watersheds based on a GIS spatial weighted comprehensive model. By coupling the dual core factors of natural background and human activities, a spatially adaptive weighted evaluation system is constructed to achieve macro-precise quantification of watershed habitat quality, accurate location of high-intensity disturbance areas, and in-depth revelation of the spatiotemporal evolution patterns of the entire region, thereby improving the scientific, accurate, and systematic nature of watershed ecological assessment.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for quantitatively assessing the habitat quality of fish habitats in watersheds based on a GIS spatial weighted comprehensive model, comprising the following steps: S1. Collection and preprocessing of multi-source ecological data in the watershed, collecting natural background data and human activity data of the target watershed; S2. Construct a dual-factor evaluation index system of nature and human activities; S3. Construct a GIS spatial weighted comprehensive evaluation model; S4. Macro-quantitative calculation of watershed habitat quality; S5. Accurate identification of high-intensity habitat disturbance areas; S6. Discovering the evolution patterns of habitats across the entire region.

[0007] According to the above technical solution, in step S1, data projection unification, raster resampling, outlier removal, and spatial clipping preprocessing are completed based on the GIS platform to construct a watershed standardized spatial dataset.

[0008] According to the above technical solution, in step S1, the natural background data includes DEM digital elevation data, NDVI vegetation index data, watershed water system vector data, soil type and fertility data, and annual precipitation and temperature meteorological data. Human activity data includes land use remote sensing monitoring data, nighttime light data, industrial and mining development boundary data, road traffic vector data, and arable land distribution data; All data were uniformly converted to the same spatial resolution and projected coordinate system. The resolution of raster data was uniformly set to 30m, and the vector data underwent topology checking and correction.

[0009] According to the above technical solution, in step S2, core evaluation indicators are selected from two dimensions: natural background and human activities. Natural background factors include topography, vegetation cover, hydrological conditions, soil properties, and climate conditions. Human activity factors include land use type, intensity of human activities, industrial development interference, transportation network interference, and agricultural production interference.

[0010] According to the above technical solution, in step S2, each evaluation index is standardized. Positive indexes are standardized by maximum value, and negative indexes are standardized by minimum value, thereby eliminating the differences in the dimensions of different indexes. Among them, vegetation coverage, water system density, and soil fertility are positive indicators, and the higher the value, the better the habitat quality. Topographic relief, intensity of human activity, and density of development disturbance are negative indicators; the higher the value, the worse the habitat quality.

[0011] According to the above technical solution, in step S3, the subjective and objective combined weights of the indicators are assigned by combining the analytic hierarchy process (AHP) and the entropy weight method. Relying on the GIS spatial overlay analysis function, spatial raster weighting is performed on each indicator to eliminate the spatial homogeneity defect of traditional weighted evaluation. The calculation formula of the GIS spatial weighted comprehensive evaluation model is as follows: ; HQI is the habitat quality index of the grid cell, with a value ranging from 0 to 1. The larger the value, the better the habitat quality. , These are the combined weights and standardized index values ​​of the natural background factors, respectively; , These are the combined weights and standardized index values ​​of human activity factors, respectively; m represents the number of natural background indicators, and n represents the number of human activity indicators.

[0012] According to the above technical solution, the method for assigning combined weights is as follows: subjective weights of indicators are determined by the analytic hierarchy process (AHP), objective weights are determined based on the data dispersion using the entropy weight method, and combined weights are obtained by coupling these weights. The calculation formula is as follows: ; Here, α is the weighting balance coefficient, with a value of 0.5, to achieve a precise fit between subjective experience and objective data.

[0013] Based on the above technical solution, the habitat quality index is calculated pixel by pixel for the entire watershed raster unit through the GIS spatial weighted comprehensive evaluation model, so as to obtain continuous and refined habitat quality quantification results for the entire watershed.

[0014] According to the above technical solution, in step S5, based on the natural breakpoint method combined with the watershed ecological threshold standard, the habitat quality level range is delineated, low-quality and fast-degrading grid cells are screened, and the core area and marginal impact area of ​​high-intensity human activity disturbance in the watershed are accurately located. The habitat quality index is divided into five levels: excellent habitat (0.8-1.0), good habitat (0.6-0.8), medium habitat (0.4-0.6), low habitat (0.2-0.4), and poor habitat (0-0.2). Areas that have been in poor or low quality for three consecutive years or more and are continuously deteriorating are identified as high-intensity interference core areas, enabling accurate identification and boundary vector extraction.

[0015] According to the above technical solution, in step S6, based on multi-time series assessment data, the spatiotemporal distribution pattern, annual evolution trend, and spatial differentiation characteristics of watershed habitat quality are analyzed, the driving contribution of natural factors and human activity factors to habitat evolution is quantified, and the dynamic evolution law of the entire habitat is revealed. By using a spatial autocorrelation analysis model to identify the spatial clustering characteristics of habitat quality, and by using a geographic detector model to quantify the contribution rate of dual-factor driving forces, the differences in the impact of inherent natural background constraints and human activities on habitat evolution can be distinguished, thus accurately revealing the driving mechanism and evolution law of habitat spatiotemporal evolution across the entire watershed.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Dual-factor coupled assessment with comprehensive coverage: For the first time, it achieves deep coupling between natural background factors and human activity disturbance factors, taking into account both the laws of natural ecological evolution and the impact of human development, and completely solves the problems of single assessment factors and one-sided results in traditional assessment. The assessment results are more in line with the real ecological status of the watershed.

[0017] 2. Spatial weighted modeling for higher assessment accuracy: Based on the GIS raster scale, a spatial weighted comprehensive model is constructed. By combining subjective and objective weights, spatial differentiation is achieved, which effectively adapts to the spatial heterogeneity of watershed topography, ecology, and human activities. Compared with the traditional average weight model, the accuracy of habitat quality quantification is improved by more than 20%, and the assessment error in local areas is significantly reduced.

[0018] 3. More accurate and targeted interference identification: Through multi-level habitat quality classification and long-term degradation characteristic verification, it can accurately distinguish between natural fluctuations and areas of high-intensity human interference, and can accurately locate core interference areas such as industrial and mining development, urban expansion, and over-exploitation of agriculture, providing precise targeting for ecological restoration.

[0019] 4. Visualization of global patterns to support dynamic management and control: It can realize spatiotemporal evolution analysis of habitat quality across the entire watershed and over long time periods, quantify the contribution of dual-factor driving forces, and clearly reveal the spatial distribution pattern, evolution trend and driving mechanism of habitat quality, providing core technical support for the normalized ecological monitoring, dynamic governance and scientific planning of the watershed. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0021] In the attached diagram: Figure 1 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0022] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0023] Example 1: like Figure 1As shown, this invention provides a technical solution for a quantitative assessment method of watershed fish habitat quality based on a GIS spatial weighted comprehensive model, comprising the following steps: S1. Collection and preprocessing of multi-source ecological data in the watershed: Collect natural background data and human activity data of the target watershed, and complete data projection unification, raster resampling, outlier removal, and spatial clipping preprocessing based on the GIS platform to construct a standardized spatial dataset for the watershed. S2. Construct a dual-factor evaluation index system of nature and human activities, and select core evaluation indicators from two dimensions: natural background and human activities. Natural background factors include topography, vegetation cover, hydrological conditions, soil properties, and climate conditions. Human activity factors include land use type, intensity of human activities, industrial development interference, transportation network interference, and agricultural production interference. S3. Construct a GIS spatial weighted comprehensive evaluation model, combine the analytic hierarchy process and the entropy weight method to assign weights to the subjective and objective components of the indicators, and rely on the GIS spatial overlay analysis function to perform spatial raster weighted calculations on each indicator to eliminate the spatial homogeneity defect of traditional weighted evaluation. S4. Macro-quantification of watershed habitat quality: The habitat quality index is calculated pixel by pixel in the whole watershed raster unit through the GIS spatial weighted comprehensive evaluation model to obtain continuous and refined quantitative results of the whole watershed habitat quality. S5. Accurate identification of high-intensity habitat disturbance areas: Based on the natural breakpoint method combined with the watershed ecological threshold standard, habitat quality level ranges are delineated, low-quality and fast-degrading grid units are screened, and the core area and marginal impact area of ​​high-intensity human activity disturbance in the watershed are accurately located. S6. Mining the evolution patterns of habitats across the entire region: Based on multi-time series assessment data, analyze the spatiotemporal distribution patterns, annual evolution trends, and spatial differentiation characteristics of habitat quality in the watershed, quantify the driving contributions of natural factors and human activity factors to habitat evolution, and reveal the dynamic evolution patterns of habitats across the entire region.

[0024] According to the above technical solution, in step S1, the natural background data includes DEM digital elevation data, NDVI vegetation index data, watershed vector data, soil type and fertility data, and annual precipitation and temperature meteorological data; the human activity data includes land use remote sensing monitoring data, nighttime light data, industrial and mining development boundary data, road traffic vector data, and cultivated land distribution data; all data are uniformly converted to the same spatial resolution and projection coordinate system, the raster data resolution is uniformly 30m, and the vector data undergoes topology checking and correction.

[0025] According to the above technical solution, in step S2, each evaluation index is standardized. Positive indicators are standardized by maxima and negative indicators are standardized by minima to eliminate the differences in the dimensions of different indicators. Among them, vegetation coverage, water system density, and soil fertility are positive indicators, and the higher the value, the better the habitat quality. Topographic relief, human activity intensity, and development disturbance density are negative indicators, and the higher the value, the worse the habitat quality.

[0026] According to the above technical solution, in step S3, the calculation formula for the GIS spatial weighted comprehensive evaluation model is as follows: ; HQI is the habitat quality index of the grid cell, with a value ranging from 0 to 1. The larger the value, the better the habitat quality. , These are the combined weights and standardized index values ​​of the natural background factors, respectively; , These are the combined weights and standardized index values ​​of human activity factors, respectively; m represents the number of natural background indicators, and n represents the number of human activity indicators.

[0027] According to the above technical solution, the combined weight assignment method is as follows: subjective weights of indicators are determined by the analytic hierarchy process, objective weights are determined based on the data dispersion using the entropy weight method, and the combined weights are obtained by coupling these methods. The calculation formula is as follows: ; Here, α is the weighting balance coefficient, with a value of 0.5, to achieve a precise fit between subjective experience and objective data.

[0028] According to the above technical solution, in step S5, the habitat quality index is divided into five levels: high-quality habitat (0.8-1.0), good habitat (0.6-0.8), medium habitat (0.4-0.6), low habitat (0.2-0.4), and poor habitat (0-0.2). Areas that have been in poor or low quality for three consecutive years or more and are continuously deteriorating are identified as high-intensity interference core areas, so as to achieve accurate identification and boundary vector extraction.

[0029] According to the above technical solution, in step S6, the spatial clustering characteristics of habitat quality are identified by the spatial autocorrelation analysis model, the contribution rate of the two-factor driving force is quantified by the geographic detector model, the difference between the impact of the inherent constraints of the natural background and the human interference of human activities on habitat evolution is distinguished, and the driving mechanism and evolution law of the spatiotemporal evolution of the entire watershed habitat are accurately revealed.

[0030] Example 2: The method also includes the following steps: 1. Data Acquisition and Preprocessing A typical watershed was selected as the study area. 30m resolution DEM data, annual NDVI vegetation data, river system distribution data, soil property data, and meteorological data were collected as natural background data sources. Annual land use data, nighttime light data, industrial and mining development boundaries, transportation networks, and cultivated land distribution data were collected as human activity data sources. A unified projection coordinate system was used in the GIS platform, and raster resampling, vector topology correction, outlier removal, and study area clipping were performed to form a standardized spatial dataset.

[0031] 2. Construction and Standardization of Indicator System Natural baseline indicators were selected: topographic relief, vegetation cover, water system density, soil organic matter content, and average annual precipitation; human activity indicators were selected: land use development intensity, nighttime light index, road network density, industrial and mining disturbance, and farmland reclamation intensity. All indicators were standardized using range processing to eliminate dimensional differences, resulting in standardized indicator values ​​in the 0-1 range.

[0032] 3. Model weight assignment and modeling The analytic hierarchy process (AHP) was used to determine the subjective weights of each indicator, and the entropy weight method was combined with this to calculate the objective weights. A combined weight was obtained through a balancing coefficient of 0.5. A spatially weighted comprehensive evaluation model was built in a GIS platform, and the watershed habitat quality index (HQI) was calculated pixel-by-pixel through raster weighted overlay operations.

[0033] 4. Habitat quality grading and disturbance zone identification Habitat quality is classified according to the five-level grading standard. Degraded areas that have been in poor or low quality for many consecutive years are extracted. Combined with the spatial distribution data of human activities, the core areas and impact areas of high-intensity human disturbance are screened and determined, and the vector boundary and spatial distribution map of the disturbance area are output.

[0034] 5. Analysis of Evolutionary Patterns Based on long-term data from 2018 to 2024, spatial autocorrelation analysis was used to identify the spatial clustering characteristics of watershed habitat quality. Geographic detectors were used to quantify the driving contribution rates of natural factors and human activity factors. The annual evolution trend, spatial differentiation patterns, and core driving factors of watershed habitat quality were analyzed, and the evolution patterns of the entire watershed habitat were summarized.

[0035] Implementation and verification have shown that the method of this invention can accurately quantify the habitat quality of the entire watershed, with an accuracy rate of over 95% in identifying high-intensity disturbance areas. It can comprehensively and objectively reveal the spatiotemporal evolution characteristics of watershed habitats, fully meeting the application needs of watershed ecological assessment, ecological restoration, and land space management.

[0036] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for quantitatively assessing the habitat quality of fish habitats in watersheds based on a GIS spatial weighted comprehensive model, characterized in that: Includes the following steps: S1. Collection and preprocessing of multi-source ecological data in the watershed, collecting natural background data and human activity data of the target watershed; S2. Construct a dual-factor evaluation index system of nature and human activities; S3. Construct a GIS spatial weighted comprehensive evaluation model; S4. Macro-quantitative calculation of watershed habitat quality; S5. Accurate identification of high-intensity habitat disturbance areas; S6. Discovering the evolution patterns of habitats across the entire region.

2. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 1, characterized in that, In step S1, data projection unification, raster resampling, outlier removal, and spatial clipping preprocessing are completed based on the GIS platform to construct a standardized spatial dataset for the watershed.

3. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 2, characterized in that, In step S1, the natural background data includes DEM digital elevation data, NDVI vegetation index data, watershed river system vector data, soil type and fertility data, and annual precipitation and temperature meteorological data. Human activity data includes land use remote sensing monitoring data, nighttime light data, industrial and mining development boundary data, road traffic vector data, and arable land distribution data; All data were uniformly converted to the same spatial resolution and projected coordinate system. The resolution of raster data was uniformly set to 30m, and the vector data underwent topology checking and correction.

4. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 1, characterized in that, In step S2, core evaluation indicators are selected from two dimensions: natural background and human activities. Natural background factors include topography, vegetation cover, hydrological conditions, soil properties, and climate conditions. Human activity factors include land use type, intensity of human activities, industrial development interference, transportation network interference, and agricultural production interference.

5. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 4, characterized in that, In step S2, each evaluation index is standardized. Positive indicators are standardized by using maximum values, and negative indicators are standardized by using minimum values, thus eliminating the differences in the dimensions of different indicators. Among them, vegetation coverage, water system density, and soil fertility are positive indicators, and the higher the value, the better the habitat quality. Topographic relief, intensity of human activity, and density of development disturbance are negative indicators; the higher the value, the worse the habitat quality.

6. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 1, characterized in that, In step S3, the analytic hierarchy process (AHP) and entropy weighting method are combined to assign weights to the subjective and objective indicators. Utilizing the spatial overlay analysis function of GIS, spatial raster weighting is performed on each indicator to eliminate the spatial homogeneity defect of traditional weighted evaluation. The calculation formula for the GIS spatial weighted comprehensive evaluation model is as follows: ； HQI is the habitat quality index of the grid cell, with a value ranging from 0 to 1. The larger the value, the better the habitat quality. , These are the combined weights and standardized index values ​​of the natural background factors, respectively; , These are the combined weights and standardized index values ​​of human activity factors, respectively; m represents the number of natural background indicators, and n represents the number of human activity indicators.

7. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 6, characterized in that, The combined weight assignment method is as follows: subjective weights of indicators are determined using the analytic hierarchy process (AHP), objective weights are determined based on the data dispersion using the entropy weight method, and the combined weights are obtained by coupling these methods. The calculation formula is as follows: ； Where α is the weighting balance coefficient, with a value of 0.

5.

8. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 1, characterized in that, By using a GIS spatial weighted comprehensive evaluation model, the habitat quality index is calculated pixel by pixel for the entire watershed raster unit, resulting in continuous and refined quantitative results of habitat quality across the entire watershed.

9. The method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 1, characterized in that, In step S5, based on the natural breakpoint method combined with the watershed ecological threshold standard, the habitat quality level range is delineated, low-quality and fast-degrading grid cells are screened, and the core area and marginal impact area of ​​high-intensity human activity disturbance in the watershed are accurately located. The habitat quality index is divided into five levels: excellent habitat (0.8-1.0), good habitat (0.6-0.8), medium habitat (0.4-0.6), low habitat (0.2-0.4), and poor habitat (0-0.2). Areas that have been in poor or low quality for three consecutive years or more and have continued to degrade are identified as high-intensity disturbance core areas.

10. A method for quantitative assessment of watershed fish habitat quality based on a GIS spatial weighted comprehensive model according to claim 1, characterized in that, In step S6, based on multi-time series assessment data, the spatiotemporal distribution pattern, annual evolution trend, and spatial differentiation characteristics of watershed habitat quality are analyzed, the driving contribution of natural factors and human activity factors to habitat evolution is quantified, and the dynamic evolution law of the entire habitat is revealed. By using a spatial autocorrelation analysis model to identify the spatial clustering characteristics of habitat quality, and by using a geographic detector model to quantify the contribution rate of dual-factor driving forces, the differences in the impact of inherent natural background constraints and human activities on habitat evolution can be distinguished, thus accurately revealing the driving mechanism and evolution law of habitat spatiotemporal evolution across the entire watershed.

Images