Resource supply, landscape pattern and interference condition-based habitat quality evaluation method

Abstract

The invention discloses a habitat quality evaluation method based on resource supply, landscape pattern and interference conditions, and the method comprises the steps: constructing a multi-level habitat quality comprehensive evaluation index system based on the resource supply-landscape pattern-interference conditions, and the multi-level habitat quality comprehensive evaluation index system comprises seven evaluation indexes in three dimensions; carrying out representation data extraction and standardization processing on the seven evaluation indexes; based on the standardized data, determining the weights of the seven evaluation indexes by using an analytic hierarchy process; based on the standardized data, analyzing a distribution structure of seven evaluation indexes by using a K-Means clustering algorithm, and determining an optimal clustering number; and calculating the habitat quality index of each cluster based on the clustering number and the weight of each evaluation index, and obtaining an evaluation result of the habitat quality.

Description

Technical Field

[0001] The present invention belongs to application fields such as ecological construction, ecological environment protection, ecosystem service assessment, and national land space planning, and in particular relates to a habitat quality assessment method based on resource supply, landscape pattern and interference conditions. Background Art

[0002] Clarifying the spatial differences in ecosystem service capacity can provide support for ecological and environmental protection in different regions. The biodiversity maintenance function is the role of ecosystems in maintaining species and genetic diversity. Through the evaluation of this indicator, key areas that can currently and in the future undertake the maintenance function of regional biodiversity (including ecosystem diversity, species diversity and genetic diversity) can be identified.

[0003] Habitat quality (HQ) is regarded as an important indicator of biodiversity. Habitat suitability models identify suitable habitats by analyzing the degree of match between biological requirements and environmental factors, and are widely used in the field of biodiversity conservation. Previous studies have shown that habitat suitability models are divided into statistical models, niche models, and mechanistic models according to whether prior data on biological distribution are required. Statistical models have their advantages in quantifying the relationship between organisms and the environment, and have good prediction results, but the models have extremely high requirements for the quantity and quality of sample data, and the ecological meaning between the model input data and output results is not clear. The niche model is based on the niche theory, and the typical representative is the Maxent model. It is simple to operate and has strong applicability, but it overemphasizes the distribution of species and ignores the complex coupling relationship between species. The mechanistic model combines the qualitative selection of environmental factors and the quantitative assignment of factor weights for evaluation, and does not require species distribution sample data. Compared with statistical models and niche models, mechanistic models have great advantages in areas lacking high-quality species distribution survey data, and the selection of environmental factors can integrate existing knowledge of biodiversity conservation. They are more practical and operational, and typical representatives are the habitat suitability index model and the InVEST habitat quality model. The land use data required by the InVEST habitat quality model is easy to obtain, the evaluation results are also highly accurate, and it has strong spatial integration capabilities, but it overemphasizes the habitat threat situation from the perspective of land use, ignoring the influence of factors such as topography, climate, and landscape pattern.

[0004] Considering that most regions currently lack comprehensive and accurate species distribution survey data and that different species of organisms such as mammals, amphibians, and reptiles have diverse demands for the scale and form of space in their life processes such as living, eating, and reproduction, there is an urgent need to provide a biological habitat quality assessment method that comprehensively considers operability and environmental factors, so as to improve the accuracy and universality of biodiversity conservation service assessments. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions, which solves the technical problems of lack of species distribution sample data, neglect of coupling relationship between species, lack of consideration of landscape pattern factors in the prior art, and subjective error problems of artificial weighting superposition in traditional assessment methods, and combines the classification method of machine learning algorithm to evaluate and grade regional habitat quality.

[0006] According to one aspect of the present invention, a method for assessing habitat quality based on resource supply, landscape pattern and disturbance conditions is provided, comprising:

[0007] Based on "resource supply-landscape pattern-disturbance conditions", a multi-level habitat quality comprehensive evaluation index system is constructed, which includes three dimensions and seven evaluation indicators;

[0008] Extracting and standardizing the characterization data of the seven evaluation indicators;

[0009] Based on the standardized data, the weights of the seven evaluation indicators are determined using the hierarchical analysis method;

[0010] Based on the standardized data, the K-Means clustering algorithm is used to analyze the distribution structure of the seven evaluation indicators and determine the optimal number of clusters;

[0011] Based on the number of clusters and the weights of each evaluation index, the habitat quality index of each cluster is calculated to obtain an evaluation result of the habitat quality.

[0012] As a further technical solution, the three dimensions include resource supply, landscape pattern and interference conditions; the resource supply includes four indicators: food resource supply, water resource supply, shelter resource supply and habitat condition supply; the landscape pattern includes two indicators: habitat connectivity and habitat heterogeneity; the interference conditions include one indicator: interference avoidance ability.

[0013] As a further technical solution, the food resource supply characterization data is primary productivity, the water resource supply characterization data is water resource distance index, the shielding resource supply characterization data is vegetation coverage, and the habitat condition supply characterization data is habitat suitability index;

[0014] The habitat connectivity characterization data is the patch density index, and the habitat heterogeneity characterization data is the Shannon diversity index;

[0015] The interference avoidance capability characterization data is an interference avoidance capability index.

[0016] As a further technical solution, characterization data extraction is performed on the seven evaluation indicators, including:

[0017] Primary productivity is the amount of organic matter accumulated by vegetation photosynthesis per unit time and per unit area;

[0018] The water resource distance index is

[0019]

[0020] Among them, F water is the water body distance attenuation index, AREA is the water patch area, d is the distance to the water body, and d max Maximum influence distance, α is a constant;

[0021] Vegetation coverage is VC = (NDVI-NDVI soil ) / (NDVI veg -NDVI soil ), where VC is the vegetation coverage, NDVI is the normalized difference vegetation index, and NDVI soil is the NDVI value of the soil, NDVI veg is the maximum NDVI value of the vegetation-covered pixel;

[0022] The habitat suitability index is assigned a value between 0 and 1 according to the land use or cover type;

[0023] The patch density index is PD = NP / A, where PD is the patch density, NP is the number of patches, and A is the total area of ​​the landscape or patches;

[0024] The Shannon diversity index is Among them, p i is the ratio of landscape patch type i;

[0025] The interference avoidance capability index is Among them, F i is the i-th interference data, W i is the weight of the i-th interference data.

[0026] As a further technical solution, the interference avoidance capability index includes a road impact inverse distance index, a cultivated land impact inverse distance index, a construction land impact inverse distance index, a night light impact distance index, a natural disaster frequency index and other interference condition indexes.

[0027] As a further technical solution, after extracting the characterization data of the seven evaluation indicators, the following standardization processing is also included:

[0028]

[0029] Among them, xscale is the normalized eigenvalue; x is the eigenvalue; x max is the maximum value of each set of eigenvalues; x min is the minimum value of each set of eigenvalues.

[0030] As a further technical solution, the K-Means clustering algorithm is used to analyze the distribution structure of the seven evaluation indicators and determine the optimal number of clusters, including:

[0031] Construct a sample data set S = {food resource supply, water resource supply, shelter condition supply, habitat condition supply, habitat connectivity, habitat heterogeneity, and disturbance avoidance ability};

[0032] The sample data set is divided into K clusters according to the attributes of each evaluation index, and K cluster centers are randomly initialized;

[0033] Each data in the sample data set is divided into the cluster with the nearest cluster center, and K cluster centers are re-divided. This process is repeated and updated iteratively until the cluster center no longer changes and clusters of the same type belong to the same level of classification. The number of clusters K determines that the habitat quality can be divided into a maximum of K levels.

[0034] The above technical solution uses the K-Means clustering algorithm to analyze the distribution structure of seven evaluation indicators. For the seven standardized evaluation indicator data, the K-Means clustering algorithm is used to select the optimal number of clusters, and the composition structure of the seven indicators of each grid unit is evaluated to reflect the status of the seven habitat conditions of resource supply, landscape pattern and interference conditions of each grid unit.

[0035] According to one aspect of the present invention, there is provided a habitat quality assessment device based on resource supply, landscape pattern and disturbance conditions, comprising:

[0036] An indicator construction module is used to construct a multi-level habitat quality comprehensive evaluation indicator system based on "resource supply-landscape pattern-disturbance conditions", and the multi-level habitat quality comprehensive evaluation indicator system includes three dimensions and seven evaluation indicators;

[0037] An indicator preprocessing module, used for extracting and standardizing the characterization data of the seven evaluation indicators;

[0038] The weight determination module is used to determine the weights of seven evaluation indicators based on the standardized data using the hierarchical analysis method;

[0039] The indicator clustering module is used to analyze the distribution structure of seven evaluation indicators based on the standardized data using the K-Means clustering algorithm to determine the optimal number of clusters;

[0040] The quality assessment module is used to calculate the habitat quality index of each cluster based on the number of clusters and the weight of each evaluation index to obtain the habitat quality assessment result.

[0041] According to one aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the habitat quality assessment method based on resource supply, landscape pattern and interference conditions.

[0042] According to one aspect of the present specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) When species survey data are insufficient, the present invention can comprehensively consider factors such as biological resource availability, habitat distribution pattern, and human disturbance activities, and construct a multi-level comprehensive evaluation index system of "resource supply-landscape pattern-disturbance conditions", providing an accurate and efficient tool for biological habitat quality assessment.

[0045] (2) The present invention establishes a set of evaluation index system including three dimensions and seven indicators. Starting from the essential characteristics of biological habitats, the habitat connectivity and habitat heterogeneity in the landscape pattern dimension are added to improve the habitat quality assessment system. In addition, the present invention also innovatively proposes indicators such as water body distance attenuation index, road distance attenuation index, cultivated land impact distance index, construction land impact distance index and their calculation methods, optimizes the calculation methods of factors such as water resource supply, agricultural activity interference, and urban construction interference, and improves the objectivity, scientificity and rationality of habitat quality assessment.

[0046] (3) The present invention adopts the K-Means clustering algorithm to perform comprehensive evaluation of indicators. Compared with the traditional weighted superposition evaluation method, it reduces the errors caused by subjective weighting and grading.

[0047] (4) Compared with the existing technical methods, the present invention makes full use of multi-source data, changes the previous shortcomings of over-reliance on species survey data and insufficient attention to landscape pattern attributes, can more objectively reflect the ecological status, and is easy to obtain. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0050] Figure 2 A multi-level comprehensive evaluation index system for habitat quality constructed for an embodiment of the present invention;

[0051] Figure 3 A spatial distribution diagram of food resource supply capacity in an embodiment of the present invention;

[0052] Figure 4 A spatial distribution diagram of water resource supply capacity in an embodiment of the present invention;

[0053] Figure 5 A spatial distribution diagram of shielding condition supply capability in an embodiment of the present invention;

[0054] Figure 6 A spatial distribution diagram of habitat supply capacity in an embodiment of the present invention;

[0055] Figure 7 is a spatial distribution map of habitat connectivity in an embodiment of the present invention;

[0056] Figure 8 is a spatial distribution diagram of habitat heterogeneity in an embodiment of the present invention;

[0057] Fig. 9 This is a spatial distribution diagram of the interference avoidance capability in an embodiment of the present invention;

[0058] Fig.10 1 is a composition structure diagram of seven indicators in an embodiment of the present invention;

[0059] Fig.11 Graph showing the spatial distribution of habitat quality levels in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0061] The present invention provides a method for assessing habitat quality based on resource supply, landscape pattern and disturbance conditions, comprising the following steps:

[0062] S1, based on the three dimensions of "resource supply-landscape pattern-disturbance conditions", a multi-level habitat quality comprehensive evaluation index system was constructed. The multi-level comprehensive evaluation index system includes seven evaluation indicators in three dimensions: resource supply, landscape pattern, and disturbance conditions. Resource supply includes four evaluation indicators: food resource supply, water resource supply, shelter condition supply, and habitat condition supply; landscape pattern includes two evaluation indicators: habitat connectivity and habitat heterogeneity; and disturbance conditions include one evaluation indicator: interference avoidance ability.

[0063] S2, using multi-source data to calculate and optimize the 7 evaluation index data in step S1, to obtain the initial grid data of the evaluation index with unified data accuracy and range.

[0064] S3, standardization of evaluation indicators. The initial data of the evaluation indicators in step S2 are standardized and mapped to a value between 0 and 1. The larger the value, the higher the positive contribution to the habitat quality. The calculation formula is as follows:

[0065]

[0066] In the formula, x scale is the normalized eigenvalue; x is the eigenvalue; x max is the maximum value of each set of eigenvalues; x min is the minimum value of each set of eigenvalues.

[0067] S4, determining the weights of the seven evaluation indicators using the analytic hierarchy process. For the seven standardized evaluation indicator data in step S3, the analytic hierarchy process is used to determine the weights of each evaluation indicator data to reflect the contribution of each evaluation indicator to the habitat quality.

[0068] S5, using the K-Means clustering algorithm to analyze the distribution structure of the seven evaluation indicators. For the 7 standardized evaluation indicator data in step S3, the K-Means clustering algorithm is used to select the optimal number of clusters to evaluate the composition structure of the seven indicators of each grid unit, reflecting the status of the seven habitat conditions of resource supply, landscape pattern and disturbance conditions of each grid unit.

[0069] The K-Means clustering algorithm is an unsupervised clustering algorithm. The sample data set of the present invention can be defined as S = {food resource supply, water resource supply, shelter condition supply, habitat condition supply, habitat connectivity, habitat heterogeneity, interference avoidance ability}. The function of the K-Means clustering algorithm is to divide the sample S containing n data into non-overlapping classes, each class represents a cluster, and the expression is

[32] . According to the habitat quality attribute characteristics of each evaluation index selected in step S1, the sample set is divided into K clusters, and K cluster centers are randomly initialized. Then, each data in the sample data set is divided into the cluster with the closest cluster center, and the K cluster centers are re-divided. This process is repeated and iterated until the cluster center no longer changes. Clusters of the same type belong to the same level of classification. The number of clusters K determines that the habitat quality can be divided into a maximum of K levels.

[0070] S6, evaluate the habitat quality index by combining the weights and structural index of the seven indicators, and perform grading and analysis. According to the number of clusters in step S5 and the weights of each evaluation indicator in step S4, calculate the habitat quality index of each cluster, where clusters of the same type belong to the same level of grading. The number of clusters will determine the maximum number of gradings, and the habitat quality grades are divided accordingly to obtain the spatial distribution results of the habitat quality grades. The calculation formula of the habitat quality index of each cluster is as follows:

[0071]

[0072] Where HQ is the habitat quality index of the cluster, K i is the corresponding value of the i-th index of cluster, W i is the weight of the ith indicator.

[0073] It should be noted that:

[0074] Habitat, also known as habitat, refers to the place where organisms survive and develop, that is, the specific environment in which individuals, populations or communities can complete their life processes. As the necessary space for their survival, the habitat provides the necessary resources for the survival and reproduction of animals. It is an indispensable guarantee for animals to carry out their life and reproduction activities, and is crucial to promoting the development and evolution of populations. Animals have the characteristics of being able to move spontaneously and independently, so that they can accurately weigh the advantages and disadvantages of the external environment in the process of adapting to the external environment. Individuals will inevitably choose the habitat that is most suitable for the survival of their species. For example, the lack of necessary resources for the survival and reproduction of populations, the unsuitability of the natural environment, geographical barriers and isolation, and the threat to their survival posed by other species will directly cause the extinction of species and the reduction of biodiversity in the area. Therefore, evaluating the quality of habitats and analyzing the relationship between species and the environment of distribution areas are the basis for biodiversity conservation.

[0075] Habitat quality refers to the ability of an ecosystem to provide living conditions suitable for individuals and populations based on the availability of survival resources. A high-quality habitat is one that maintains a relatively complete structure and function within the historical dynamic range. Some habitat islands have been isolated for a long time, which seriously hinders biological activities and exchanges between groups, easily leading to obstructed exchanges between populations and inbreeding, causing the weakening of wild animals' adaptability to environmental changes and reduced reproduction rates. Therefore, the selection of factors in the landscape pattern dimension is particularly important in assessing habitat quality. The connectivity of habitats has an important impact on the protection and improvement of biodiversity. The rapid expansion of cities has directly led to the relative isolation of habitats. The habitat has become smaller and less fertile with the fragmentation of the landscape, which has seriously hindered the genetic exchange of species between "islands". The stronger the connectivity between habitats, the larger the habitat area that organisms can reach through migration, and the easier it is to spread to suitable habitats, expand the habitat range of populations, and increase the overall number of populations. At the same time, the enhancement of connectivity can also reduce the competition pressure within and between species in habitat patches and promote genetic exchange between habitat patches. Therefore, a habitat with strong connectivity will have a large number of organisms and transit organisms that use this patch as a stepping stone, and the biological species are diverse. Habitat heterogeneity is considered to be a factor closely related to biodiversity, uniformity and dominance. Landscape heterogeneity refers to the non-uniformity and variability of landscape characteristics, including landscape diversity, landscape uniformity and landscape dominance. The higher the diversity of habitats, the lower the uniformity, and the higher the species diversity and richness. For example, the vertical structure of woodlands is complex, providing rich living resources, including food, habitats and shelters; appropriately improving the vegetation type and structure at the edge of farmland can attract various biological species; shrubs can provide unique habitats and food sources for a variety of organisms; urbanization threatens the habitats of organisms, but the complexity of urban landscapes also accommodates and maintains the survival of many organisms.

[0076] Disturbance conditions mainly refer to human disturbance activities in the study area, and the intensity of such activities will have a significant negative impact on habitat quality. Human activities have brought a lot of industrial and domestic pollution. The interference of land development, grazing and other activities on natural ecosystems has also broken the original functions and structures of the ecosystems, thereby affecting the number and distribution patterns of plant and animal species. Studies have shown that human disturbance can significantly change the environmental conditions and habitat quality of the habitat. The degree of social and economic prosperity, population density, education level, transportation and communication conditions in the areas where wild animals live will have a huge impact on their activity space, accelerate the degradation of habitats, reduce habitat quality, and deteriorate habitat suitability.

[0077] Based on the above considerations, the embodiment of the present invention constructs a multi-level comprehensive evaluation index system, including 7 indicators in three dimensions: resource supply, landscape pattern and interference conditions. The resource supply includes four indicators: food resource supply, water resource supply, shelter resource supply and habitat condition supply; the landscape pattern includes two indicators: habitat connectivity and habitat heterogeneity; and the interference condition includes one indicator: interference avoidance ability.

[0078] In the embodiment of the present invention, the food resource supply characterization data is primary productivity. Specifically, it is characterized by net primary productivity (NPP), which is the amount of organic matter accumulated per unit time and per unit area by vegetation photosynthesis, reflecting the ability of vegetation to convert solar energy into organic matter, representing the potential possibility of organisms to obtain food, and obtaining NPP data within the scope of the study area to obtain the initial data of the food resource supply capacity indicator.

[0079] In an embodiment of the present invention, the water resource supply characterization data is a water resource distance index. Specifically, the water resource distance index represents the difficulty of biological access to water resources. The service scope and capacity of water resources are calculated by comprehensively considering the area and distance of water patches, and a water body distance attenuation index calculation model is constructed that is proportional to the size of the water body and inversely proportional to the distance from the water body, and the initial data of the water resource distance index is obtained by calculation.

[0080] In the embodiment of the present invention, the shading condition characterization data is vegetation coverage. Specifically, the vegetation coverage reflects the vegetation quality and the growth dynamics of the vegetation community, represents the degree of surface vegetation shading and the living and protection conditions provided for organisms, and the initial vegetation coverage data is obtained after calculation based on the normalized vegetation index data.

[0081] In the embodiment of the present invention, the habitat condition supply characterization data is a habitat suitability index. Specifically, the habitat suitability index represents the supply capacity of different land use or cover types to provide habitats such as nesting conditions. The initial data of the habitat suitability index is obtained by combining the land use and cover raster data with Table 1 and assigning values ​​according to the land use or cover type.

[0082] Table 1 Habitat conditions-habitat suitability index assignment table

[0083] LULC type Habitat Suitability Index 1 arable land 0.5 2 woodland 1 3 Thicket 1 4 grassland 0.75 5 Waters 1 6 Snow and ice 0.2 7 wasteland 0.2 8 Construction land 0 9 Wetlands 0.8

[0084] It should be noted that the habitat suitability index is the habitat suitability assigned to each LULC, and its value range is 0 to 1, where 1 represents habitat and 0 represents non-habitat.

[0085] In the embodiment of the present invention, the habitat connectivity characterization data is a patch density index. Specifically, the patch density index is used to characterize the number of patches per unit area in the neighborhood. The present invention characterizes the fragmentation and spatial heterogeneity of the neighborhood habitat, and the initial habitat connectivity data is obtained by calculating the patch density data.

[0086] In the embodiment of the present invention, the habitat heterogeneity characterization data is the Shannon diversity index. Specifically, the Shannon diversity index is used to characterize and reflect the diversity and heterogeneity of landscape types. Here, the diversity and heterogeneity of landscape types in the neighborhood of the patch are characterized. The initial data of habitat heterogeneity is obtained by calculating the Shannon diversity index through land use data.

[0087] In an embodiment of the present invention, the interference avoidance capability is road traffic interference, agricultural activity interference, urban and rural construction interference, light pollution interference, natural disaster interference and other interferences, wherein the road traffic interference characterization data is the road impact distance index, the agricultural activity interference characterization data is the cultivated land impact distance index, the urban and rural construction interference characterization data is the construction land impact distance index, the light pollution interference characterization data is the night light impact distance index, and the natural disaster interference characterization data is the natural disaster frequency index.

[0088] The interference avoidance capability is characterized by the road impact inverse distance index, cultivated land impact inverse distance index, construction land impact inverse distance index, night light impact distance index, natural disaster frequency index and other interference data. The weight of each interference data is determined by the hierarchical analysis method, and the weighted sum is used to obtain the final interference avoidance capability index. The specific processing method of each data is as follows:

[0089] 1) Road impact inverse distance index, which characterizes the impact of road and traffic noise on the quality of natural habitats, biological utilization patterns and spatial distribution. The road inverse distance attenuation index is calculated based on the road vector data of the study area, and two inverse distance attenuation methods are provided: linear attenuation and exponential attenuation:

[0090] ①The linear attenuation calculation formula is as follows:

[0091]

[0092] ②The exponential decay calculation formula is as follows:

[0093]

[0094] In the formula, F road is the road inverse distance attenuation index, d is the distance from the road (in km), max is the maximum impact distance of road impact (unit: km), and the recommended value is 2 km. α is the maximum impact index of different types of roads, which can be 1 in linear attenuation and is recommended to be greater than 1 in exponential attenuation.

[0095] 2) The inverse distance index of cultivated land impact characterizes the degree of impact of agricultural activities on habitat quality. The influence of cultivated land is calculated by combining the area and distance of cultivated land patches. The cultivated land inverse distance decay index is calculated based on the cultivated land coverage data of the study area, and two inverse distance decay methods are provided: linear decay and exponential decay:

[0096] ①The linear attenuation calculation formula is as follows:

[0097]

[0098] ②The exponential decay calculation formula is as follows:

[0099]

[0100] In the formula, F farmland is the inverse distance decay index of cultivated land, d is the distance to the cultivated land (in km), AREA is the area of ​​cultivated land patches (in km 2 ), α is a constant, which can be 1.

[0101] 3) The inverse distance index of construction land impact characterizes the impact of urban expansion and construction activities on natural habitats and biodiversity. The area and distance of construction land patches are used to calculate the influence of construction land. The inverse distance decay index of construction land is calculated based on the construction land coverage data of the study area, and two inverse distance decay methods are provided: linear decay and exponential decay:

[0102] ①The linear attenuation calculation formula is as follows:

[0103]

[0104] ②The exponential decay calculation formula is as follows:

[0105]

[0106] In the formula, F urban is the inverse distance attenuation index of construction land, AREA is the area of ​​construction land patches (in km 2 ), d is the distance to the construction land (in km), and α is a constant that can be 1.

[0107] 4) Night light impact index, which characterizes the impact of urban human activity intensity, industrialization level and urbanization level on the quality of natural habitats. The initial data of the night light impact index is obtained after reverse normalization based on remote sensing night light data.

[0108] 5) Natural disaster frequency index, which characterizes the impact of natural disasters such as heavy rain, landslide, and mud-rock flow on organisms. The initial data of the natural disaster frequency index is obtained based on the spatialization and reverse standardization of natural disaster statistical data.

[0109] 6) The selection of other interference condition indicators can be based on the characteristics of the study area, and the data can be processed by forward or reverse standardization.

[0110] If the interference condition indicator is positive:

[0111]

[0112] If the interference condition indicator is negative:

[0113]

[0114] In the formula, x scale is the normalized eigenvalue; x is the eigenvalue; x max is the maximum value of each set of eigenvalues; x min is the minimum value of each set of eigenvalues.

[0115] 7) The calculation formula of interference avoidance capability index is as follows:

[0116]

[0117] Where F is the interference avoidance capability index, F i is the i-th interference data, W i is the weight of the i-th interference data, and the weight of each interference data is determined by the hierarchical analysis method.

[0118] As a preferred embodiment, the embodiment of the present invention is described by taking the habitat quality assessment based on resource supply, landscape pattern and disturbance conditions in Wuhan City, Hubei Province in 2020 as an example. Figure 1 , a habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions provided by an embodiment of the present invention comprises the following steps:

[0119] S1, on the basis of fully understanding the essential characteristics and connotation of habitat quality, a multi-level comprehensive evaluation index system is constructed from three dimensions of "resource supply-landscape pattern-disturbance conditions" and based on 7 evaluation indicators;

[0120] Specifically, the multi-level comprehensive evaluation index system includes seven evaluation indicators in three dimensions: resource supply, landscape pattern and disturbance conditions. Resource supply includes four evaluation indicators: food resource supply, water resource supply, shelter condition supply and habitat condition supply; landscape pattern includes two evaluation indicators: habitat connectivity and habitat heterogeneity; disturbance conditions include one evaluation indicator: interference avoidance ability; the seven evaluation indicators are: food resource supply, water resource supply, shelter condition supply, habitat condition supply, habitat connectivity, habitat heterogeneity and interference avoidance ability. For details of each indicator and its representation data, see Figure 2 and Table 2.

[0121] Table 2 Sources of initial data for each evaluation index

[0122]

[0123]

[0124] Among them, “+” represents a positive indicator and “-” represents a negative indicator.

[0125] S2, using multi-source data to calculate and optimize the 7 evaluation index data in step S1, to obtain the initial grid data of the evaluation index with unified data accuracy and range;

[0126] Specifically, the initial data sources of each evaluation index are shown in Table 2. During the extraction process, the 7 evaluation indexes need to be uniformly normalized. The uniform normalization process includes projection, resampling, and clipping to ensure the uniformity of the spatial information and range of the initial data of the 7 evaluation indexes. The calculation method is as follows:

[0127] (1) Food resource supply: Net primary productivity (NPP) data is used. After unified standardization, the initial data of food resource supply capacity indicators are obtained.

[0128] (2) Water resources supply is represented by the water resources distance index, and the calculation formula is as follows:

[0129]

[0130] In the formula, F water is the water body distance attenuation index, AREA is the water patch area (unit km 2 ), d is the distance to the water area (in km), d maxMaximum impact distance (unit: km), α is a constant and can be 1.

[0131] Specifically, considering the impact of water resources on biological habitats and the characteristics of the study area, the maximum impact distance of water resources is taken as 2km and α is taken as 1.

[0132] (3) Shading condition supply, represented by vegetation coverage, is calculated as follows:

[0133] VC=(NDVI-NDVI soil ) / (NDVI veg -NDVI soil )

[0134] In the formula, VC is the vegetation coverage, NDVI is the normalized difference vegetation index, and NDVI is soil is the NDVI value of the soil, NDVI veg is the maximum NDVI value of the vegetation cover pixel.

[0135] Specifically, the ArcGIS raster calculator was used to calculate the normalized NDVI data to obtain the initial data of the shielding condition supply capacity in the study area.

[0136] (4) Habitat supply, represented by the habitat suitability index, was obtained by reclassifying the normalized CLCD land use coverage data according to Table 1 using ArcGIS to obtain the initial data on habitat supply capacity.

[0137] (5) Habitat connectivity, represented by the patch density index, is calculated as follows:

[0138] PD=NP / A

[0139] Where PD is the patch density, NP is the number of patches, and A is the total area of ​​the landscape or patches.

[0140] Specifically, the Fragstats4.2 software was used to calculate the normalized CLCD land use coverage data to obtain the patch density index PD.

[0141] (6) Habitat heterogeneity, represented by the Shannon diversity index, is calculated as follows:

[0142]

[0143] Where SHDI is the Shannon diversity index, p i is the ratio of landscape patch type i.

[0144] Specifically, the standardized CLCD land use coverage data were calculated using Fragstats4.2 software to obtain the Shannon diversity index SHDI.

[0145] (7) Interference avoidance capability, including road traffic interference, agricultural activity interference, urban and rural construction interference, light pollution interference, natural disaster interference and other interference, is represented by road impact distance index, cultivated land impact distance index, construction land impact distance index, night light impact distance index, natural disaster frequency index and other interference data, and the weight of each interference data is determined by the hierarchical analysis method, and the weighted sum is used to obtain the final interference avoidance capability index. The specific processing methods of each data are as follows:

[0146] 1) Road influence distance index: The road distance attenuation index is calculated based on the road vector data of the study area. The linear attenuation method is selected in the embodiment of the present application. The calculation formula is as follows:

[0147]

[0148] In the formula, F road is the road distance attenuation index, d is the distance from the road (in km), max is the maximum impact distance of the road (unit: km), and the recommended value is 2 km. α is the maximum impact index of different types of roads, and the recommended value is 1.

[0149] Specifically, considering the impact of road traffic on biological habitats and the characteristics of the study area, the maximum impact distance of the road is taken as 2km and α is taken as 1.

[0150] 2) The cultivated land impact distance index characterizes the impact of agricultural activities on habitat quality. The linear attenuation method is selected and the calculation formula is as follows:

[0151]

[0152] In the formula, F farmland is the cultivated land distance attenuation index, d is the distance to the cultivated land (in km), AREA is the cultivated land patch area (in km 2 ), α is a constant, which can be 1.

[0153] Specifically, considering the impact of agricultural activities on biological habitats and the characteristics of the study area, α is taken as 1.

[0154] 3) Construction land impact distance index, which represents the impact of urban expansion and construction activities on natural habitats and biodiversity, selects a linear attenuation method, and the calculation formula is as follows:

[0155]

[0156] In the formula, F urban is the distance attenuation index of construction land, AREA is the area of ​​construction land patches (in km 2), d is the distance to the construction land (in km), and α is a constant that can be 1.

[0157] Specifically, considering the impact of urban and rural construction activities on biological habitats and the characteristics of the study area, α is taken as 1.

[0158] 4) Night light impact index: The initial data of the night light impact index are obtained by reverse standardization of the remote sensing night light data using ArcGIS.

[0159] 5) Natural disaster frequency index: ArcGIS is used to spatialize and reverse standardize natural disaster statistical data to obtain the initial data of natural disaster frequency index.

[0160] 6) The selection of other interference condition indicators can be based on the characteristics of the study area, and the data can be processed by forward or reverse standardization.

[0161] Specifically, based on the characteristics of the study area and data availability, no other interference condition indicators are selected in the embodiments of the present application.

[0162] 7) The calculation formula of interference avoidance capability index is as follows:

[0163]

[0164] Where F is the interference avoidance capability index, F i is the i-th interference data, W i is the weight of the i-th interference data, and the weight of each interference data is determined by the hierarchical analysis method.

[0165] Specifically, a judgment matrix is ​​constructed according to the relative importance of each interference data, as shown in Table 3.

[0166] Table 3 Interference avoidance capability-interference data judgment matrix

[0167]

[0168]

[0169] The second is the consistency test. According to the results of the AHP calculation, the consistency ratio (CR) = 0.023 < 0.1, which passes the consistency test. The weight values ​​of each interference data calculated by the AHP are shown in Table 4.

[0170] Table 4 Interference avoidance capability-interference data weight table

[0171]

[0172] S3, standardization of evaluation indicators. The initial data of the evaluation indicators in step S2 are standardized in terms of dimensional units, and the calculation formula is as follows:

[0173] If the evaluation index is positive:

[0174]

[0175] If the evaluation index is negative:

[0176]

[0177] In the formula, x scale is the normalized eigenvalue; x is the eigenvalue; x max is the maximum value of each set of eigenvalues; x min is the minimum value of each set of eigenvalues.

[0178] Specifically, according to the different characteristics of the indicators' impact on habitat quality, each indicator is divided into positive indicators and negative indicators. The greater the value of the positive indicator, the better the habitat quality of the area, while the opposite is true for negative indicators. The properties of each indicator are shown in Table 2, and the standardized data of each evaluation indicator are shown in Figure 3-9 .

[0179] S4, for the 7 standardized evaluation index data in step S3, the weight of each evaluation index data is determined by using the hierarchical analysis method.

[0180] Specifically, a judgment matrix is ​​constructed according to the relative importance of each evaluation index, as shown in Table 5.

[0181] Table 5 Judgment matrix of habitat quality evaluation indicators

[0182]

[0183]

[0184] The second is the consistency test. According to the results of the AHP calculation, the consistency ratio (CR) = 0.024 < 0.1, which passes the consistency test. The weight values ​​of each interference data calculated by the AHP are shown in Table 6.

[0185] Table 6 Weight table of habitat quality evaluation indicators

[0186]

[0187] S5, using K-Means clustering algorithm to analyze the distribution structure of the seven evaluation indicators. For the 7 standardized evaluation indicator data in step S3, the K-Means clustering algorithm is used to select the optimal number of clusters. In the embodiment of the present application, K=10 is set to evaluate the composition structure of the seven indicators of each grid unit. Fig.10 .

[0188] S6, evaluate the habitat quality index by combining the weights and structural index of the seven indicators, and perform grading and analysis. According to the number of clusters in step S5 and the weights of each evaluation indicator in step S4, calculate the habitat quality index of each cluster, where clusters of the same type belong to the same level of grading. The number of clusters will determine the maximum number of gradings, and the habitat quality grades are divided accordingly to obtain the spatial distribution results of the habitat quality grades. The calculation formula of the habitat quality index of each cluster is as follows:

[0189]

[0190] Where HQ is the habitat quality index of the cluster, K i is the corresponding value of the i-th index of cluster, W i is the weight of the ith indicator.

[0191] Specifically, the regional habitat quality is divided into ten levels, and the habitat quality index for each level is obtained, see Table 7.

[0192] Table 7 Habitat quality index classification table

[0193] cluster 8 6 0 3 4 7 9 2 1 5 Habitat quality level 1 2 3 4 5 6 7 8 9 10 Habitat Quality Index 0.239 0.414 0.440 0.483 0.529 0.557 0.574 0.578 0.656 0.659

[0194] The spatial distribution results of habitat quality grades in the study area are obtained. The larger the grade, the higher the habitat quality. Fig.11 . Among them, the areas with the best habitat quality are mainly the core areas of high-quality forest habitats on the mountains in the north, east and south of Wuhan City, and the complex habitats composed of shrubs, wetlands, farmlands and other ecosystems closely surrounding large water bodies such as Liangzi Lake and the Yangtze River. The land use and food resources in these areas can meet the survival needs of organisms, but the habitats are obviously divided by transportation routes. The areas with better habitat quality are mainly forests and farmlands. These forests and farmlands surround the areas with the best habitat quality and play the role of buffer zones between habitats and human interference. The areas with better habitat quality in the north are large and concentrated, while the areas with better habitat quality in the south are small and scattered. It can be seen that the habitat quality in Wuhan has formed a relatively obvious geographical difference. The area with habitat quality level of eight or above accounts for about 34.5%, and the area with habitat quality level of three or below accounts for about 24.3%. There is still room for improvement in the regional habitat quality.

[0195] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a habitat quality assessment device based on resource supply, landscape pattern and interference conditions, which is used to execute the habitat quality assessment device based on resource supply, landscape pattern and interference conditions in the above method embodiment.

[0196] The device comprises: an index construction module, which is used to construct a multi-level habitat quality comprehensive evaluation index system based on "resource supply-landscape pattern-disturbance conditions", and the multi-level habitat quality comprehensive evaluation index system includes three dimensions and seven evaluation indicators; an index preprocessing module, which is used to extract and standardize the characterization data of the seven evaluation indicators; a weight determination module, which is used to determine the weights of the seven evaluation indicators based on the standardized data by using the hierarchical analysis method; an index clustering module, which is used to analyze the distribution structure of the seven evaluation indicators based on the standardized data by using the K-Means clustering algorithm to determine the optimal number of clusters; and a quality assessment module, which is used to calculate the habitat quality index of each cluster based on the number of clusters and the weights of each evaluation indicator to obtain the assessment result of the habitat quality.

[0197] The habitat quality assessment device based on resource supply, landscape pattern and interference conditions provided by the embodiment of the present invention adopts the above-mentioned several modules and can comprehensively consider factors such as biological resource availability, habitat distribution pattern and human interference activities when species survey data is insufficient, and construct a set of "resource supply-landscape pattern-interference conditions" multi-level comprehensive evaluation index system, which provides an accurate and efficient tool for biological habitat quality assessment. At the same time, indicators such as water body distance attenuation index, road distance attenuation index, cultivated land impact distance index, construction land impact distance index and their calculation methods are proposed, and the calculation methods of factors such as water source resource supply, agricultural activity interference, and urban construction interference are optimized, thereby improving the objectivity, scientificity and rationality of habitat quality assessment. The K-Means clustering algorithm is also used for comprehensive evaluation of indicators. Compared with the traditional weighted superposition evaluation method, the error caused by human subjective weighting and classification is reduced.

[0198] It should be noted that the device embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference lies in the setting of corresponding functional modules, and the principles thereof are basically the same as the principles of the above-mentioned device embodiments provided by the present invention. As long as technical personnel in this field refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned device embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above-mentioned device embodiments to obtain corresponding device class embodiments for implementing the methods in other method class embodiments.

[0199] Based on the same inventive concept as the above-mentioned embodiment, an embodiment of the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the habitat quality assessment method based on resource supply, landscape pattern and interference conditions.

[0200] Based on the same inventive concept as the above-mentioned embodiment, the embodiment of the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the habitat quality assessment method based on resource supply, landscape pattern and interference conditions.

[0201] In summary, the present invention proposes a habitat quality assessment model based on resource supply, landscape pattern and disturbance conditions, including constructing a multi-level habitat quality comprehensive evaluation index system based on the three dimensions of "resource supply-landscape pattern-disturbance conditions"; using multi-source data to calculate and optimize 7 evaluation index data; standardizing the index data; using the analytic hierarchy process to determine the weights of the 7 evaluation indicators; using the K-Means clustering algorithm to analyze the distribution structure of the seven evaluation indicators; comprehensively evaluating the habitat quality index based on the weights and structural index of the 7 indicators, and performing grade division and analysis. The present invention uses multi-source data to more objectively characterize the essential characteristics of habitat quality from the perspective of biological habitat background; the optimization of indicators and evaluation methods improves the objectivity, scientificity and rationality of habitat quality assessment; comprehensively considering factors such as biological resource availability, habitat distribution pattern and human disturbance activities, a set of scientific and objective habitat quality evaluation index system is constructed, the process is clear, it has strong universality, and it is easy to promote and apply in regional scale ecological research.

[0202] Although the present invention is described herein with reference to a number of illustrative embodiments of the present invention, it will be appreciated that those skilled in the art may devise many other modifications and implementations that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, drawings, and claims, a variety of variations and modifications may be made to the components and / or layout of the subject combination layout. In addition to the variations and modifications made to the components and / or layout, other uses will also be apparent to those skilled in the art.

[0203] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions, characterized by: include: Based on "resource supply-landscape pattern-disturbance conditions", a multi-level habitat quality comprehensive evaluation index system is constructed, which includes three dimensions and seven evaluation indicators; Extracting and standardizing the characterization data of the seven evaluation indicators; Based on the standardized data, the weights of the seven evaluation indicators are determined using the hierarchical analysis method; Based on the standardized data, the K-Means clustering algorithm is used to analyze the distribution structure of the seven evaluation indicators and determine the optimal number of clusters; Based on the number of clusters and the weights of each evaluation index, the habitat quality index of each cluster is calculated to obtain an evaluation result of the habitat quality.

2. The habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions according to claim 1, characterized in that: The three dimensions include resource supply, landscape pattern and interference conditions; the resource supply includes four indicators: food resource supply, water resource supply, shelter resource supply and habitat condition supply; the landscape pattern includes two indicators: habitat connectivity and habitat heterogeneity; the interference conditions include one indicator: interference avoidance ability.

3. The habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions according to claim 2, characterized in that: The food resource supply characterization data is primary productivity, the water resource supply characterization data is water resource distance index, the shielding resource supply characterization data is vegetation coverage, and the habitat condition supply characterization data is habitat suitability index; The habitat connectivity characterization data is the patch density index, and the habitat heterogeneity characterization data is the Shannon diversity index; The interference avoidance capability characterization data is an interference avoidance capability index.

4. The habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions according to claim 3 is characterized in that: Characterization data extraction is performed on the seven evaluation indicators, including: Primary productivity is the amount of organic matter accumulated by vegetation photosynthesis per unit time and per unit area; The water resource distance index is Among them, F water is the water body distance attenuation index, AREA is the water patch area, d is the distance to the water body, and d max Maximum influence distance, α is a constant; Vegetation coverage is VC = ( NDVI-NDVI soil ) / (NDVI veg -NDVI soil ), where VC is the vegetation coverage, NDVI is the normalized difference vegetation index, and NDVI soil is the NDVI value of the soil, NDVI veg is the maximum NDVI value of the vegetation-covered pixel; The habitat suitability index is assigned a value between 0 and 1 according to the land use or cover type; The patch density index is PD = NP / A, where PD is the patch density, NP is the number of patches, and A is the total area of ​​the landscape or patches; The Shannon diversity index is Among them, p i is the ratio of landscape patch type i; The interference avoidance capability index is Among them, F i is the i-th interference data, W i is the weight of the i-th interference data.

5. The habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions according to claim 4, characterized in that: The interference avoidance capability index includes the road impact inverse distance index, the cultivated land impact inverse distance index, the construction land impact inverse distance index, the night light impact distance index, the natural disaster frequency index and other interference condition indexes.

6. The habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions according to claim 4, characterized in that: After characterizing the data extraction of the seven evaluation indicators, the following standardization processing is also included: Among them, x scale is the normalized eigenvalue; x is the eigenvalue; x max is the maximum value of each set of eigenvalues; x min is the minimum value of each set of eigenvalues.

7. The habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions according to claim 1, characterized in that: The K-Means clustering algorithm is used to analyze the distribution structure of seven evaluation indicators and determine the optimal number of clusters, including: Construct a sample data set S = {food resource supply, water resource supply, shelter condition supply, habitat condition supply, habitat connectivity, habitat heterogeneity, and disturbance avoidance ability}; The sample data set is divided into K clusters according to the attributes of each evaluation index, and K cluster centers are randomly initialized; Each data in the sample data set is divided into the cluster with the nearest cluster center, and K cluster centers are re-divided. This process is repeated and updated iteratively until the cluster center no longer changes and clusters of the same type belong to the same level of classification. The number of clusters K determines that the habitat quality can be divided into a maximum of K levels.

8. A habitat quality assessment device based on resource supply, landscape pattern and disturbance conditions, characterized in that: include: An indicator construction module is used to construct a multi-level habitat quality comprehensive evaluation indicator system based on "resource supply-landscape pattern-disturbance conditions", and the multi-level habitat quality comprehensive evaluation indicator system includes three dimensions and seven evaluation indicators; An indicator preprocessing module, used for extracting and standardizing the characterization data of the seven evaluation indicators; The weight determination module is used to determine the weights of seven evaluation indicators based on the standardized data using the hierarchical analysis method; The indicator clustering module is used to analyze the distribution structure of seven evaluation indicators based on the standardized data using the K-Means clustering algorithm to determine the optimal number of clusters; The quality assessment module is used to calculate the habitat quality index of each cluster based on the number of clusters and the weight of each evaluation index to obtain the habitat quality assessment result.

9. An electronic device, characterized in that: It includes a memory and a processor, the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the habitat quality assessment method based on resource supply, landscape pattern and interference conditions as described in any one of claims 1 to 7.

10. A non-transitory computer readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which enable the computer to execute the steps of the habitat quality assessment method based on resource supply, landscape pattern and disturbance conditions as described in any one of claims 1 to 7.

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