Biodiversity drawing method and device, electronic equipment and storage medium

Abstract

The invention relates to a biodiversity charting method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining remote sensing observation data, foundation observation data and species information data, constructing an ecological data cube, extracting statistical features, frequency features and phenological features according to the ecological data cube, and calculating the statistical features, the frequency features and the phenological features according to the statistical features, the frequency features and the phenological features; the method comprises the following steps: constructing a species diversity space map, a phylogenetic diversity map and a global genetic diversity map, fusing the maps to obtain a fusion result, identifying a high-diversity region and a low-protection region according to the fusion result, and evaluating the high-diversity region and the low-protection region to obtain an evaluation result. Therefore, the problems that related technologies depend on the number of samples and data precision, phylogenetic and genetic layer patterns are difficult to reveal, and global scale decision and management requirements are difficult to meet are solved, multi-source satellite remote sensing and foundation data can be fused, and the comprehensiveness and scientificity of diversity evaluation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biodiversity monitoring, and in particular to a biodiversity mapping method and device, an electronic device, and a storage medium. BACKGROUND

[0002] With the continuous loss of global biodiversity, the scientific and policy communities have an increasing demand for species conservation and sustainable management of ecosystems. Spatialized diversity information is considered a key basis for setting conservation priorities and assessing the impact of human activities.

[0003] The species diversity mapping method in the related art is mostly based on species occurrence point data, and uses a niche model (such as MaxEnt) to predict the distribution range, and then superimposes environmental variables to identify potential habitats.

[0004] However, such methods usually rely on the number of point samples and the accuracy of environmental data, have limited spatial resolution, and are difficult to reveal the biodiversity patterns at the phylogenetic and genetic levels, and are difficult to meet the needs of global-scale decision-making and management. SUMMARY

[0005] The present application provides a biodiversity mapping method, device, electronic device, and storage medium to solve the problem that the related art relies on the number of samples and the accuracy of data, and is difficult to reveal the patterns at the phylogenetic and genetic levels, and is difficult to meet the needs of global-scale decision-making and management. The present application can integrate multi-source satellite remote sensing and ground-based data to improve the comprehensiveness and scientificity of diversity assessment.

[0006] The first aspect of the present application provides a biodiversity mapping method, comprising the following steps:

[0007] Obtaining remote sensing observation data, ground-based observation data, and species information data

[0008] Constructing an ecological data cube based on the remote sensing observation data, ground-based observation data, and species information data, and extracting statistical features, frequency features, and phenological features according to the ecological data cube;

[0009] Constructing a species diversity spatial map, a phylogenetic diversity map, and a global genetic diversity map based on the statistical features, the frequency features, and the phenological features, fusing the species diversity spatial map, the phylogenetic diversity map, and the global genetic diversity map to obtain a fusion result, identifying high diversity areas and low protection areas according to the fusion result, and evaluating the high diversity areas and low protection areas to obtain an evaluation result.

[0010] Optionally, in some embodiments, the constructing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map based on the statistical feature, the frequency feature and the phenological feature comprises:

[0011] The species habitat suitability model is constructed based on the statistical feature, the frequency feature and the phenological feature by using a preset maximum entropy model and a preset random forest model, and the species diversity spatial map is obtained based on the species habitat suitability model.

[0012] The phylogenetic diversity index is calculated based on the statistical feature, the frequency feature and the phenological feature, and the phylogenetic diversity map is obtained based on the phylogenetic diversity index.

[0013] The expected heterozygosity and / or the nucleotide diversity are calculated based on the statistical feature, the frequency feature and the phenological feature, and the global genetic diversity map is obtained based on the expected heterozygosity and / or the nucleotide diversity.

[0014] Optionally, in some embodiments, the fusing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain a fusion result comprises:

[0015] The species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map are fused based on a preset fusion formula to obtain a fusion result, wherein the preset fusion formula is:

[0016] D(x,y)=w1·P AOH (x,y)+w2·PD(x,y)+w3·He(x,y)

[0017] wherein D(x,y) is a value of a comprehensive diversity index at a position (x,y), P AOH (x,y) is a species richness at the position (x,y), PD(x,y) is a phylogenetic diversity at the position (x,y), He(x,y) is a genetic diversity at the position (x,y), w1 is a fusion weight of the species diversity spatial map, w2 is a fusion weight of the phylogenetic diversity map, and w3 is a fusion weight of the global genetic diversity map.

[0018] Optionally, in some embodiments, the remote sensing observation data comprises at least one of high-resolution optical image data, synthetic aperture radar data, laser radar data, night light data, terrain data, climate reanalysis data and future simulation product data.

[0019] The ground observation data includes at least one of weather station record data, flux tower data, ecological station sample plot monitoring data, near-ground phenology camera time series data, and hyperspectral observation data.

[0020] Optionally, in some embodiments, the statistical features include at least one of annual mean, annual standard deviation, annual coefficient of variation, interannual variability, seasonal amplitude, and greenness growing season length.

[0021] The frequency features include at least one of high temperature frequency and low temperature frequency.

[0022] The second aspect embodiment of the present application provides a biodiversity mapping device, comprising:

[0023] An acquisition module is configured to acquire remote sensing observation data, ground observation data, and species information data.

[0024] A construction module is configured to construct an ecological data cube based on the remote sensing observation data, the ground observation data, and the species information data, and extract statistical features, frequency features, and phenological features from the ecological data cube.

[0025] A mapping module is configured to construct a species diversity spatial map, a phylogenetic diversity map, and a global genetic diversity map based on the statistical features, the frequency features, and the phenological features, fuse the species diversity spatial map, the phylogenetic diversity map, and the global genetic diversity map to obtain a fusion result, identify high diversity areas and low protection areas based on the fusion result, and evaluate the high diversity areas and the low protection areas to obtain an evaluation result.

[0026] Optionally, in some embodiments, the mapping module comprises:

[0027] A first construction unit is configured to construct a species habitat suitability model based on the statistical features, the frequency features, and the phenological features, using a preset maximum entropy model and a preset random forest model, and obtain the species diversity spatial map based on the species habitat suitability model.

[0028] A second construction unit is configured to calculate a phylogenetic diversity index based on the statistical features, the frequency features, and the phenological features, and obtain the phylogenetic diversity map based on the phylogenetic diversity index.

[0029] A third construction unit is configured to calculate expected heterozygosity and / or nucleotide diversity based on the statistical features, the frequency features, and the phenological features, and obtain the global genetic diversity map based on the expected heterozygosity and / or the nucleotide diversity.

[0030] Optionally, in some embodiments, the fusion of the species diversity spatial map, the phylogenetic diversity map, and the global genetic diversity map to obtain the fusion result includes:

[0031] Based on a preset fusion formula, the spatial map of species diversity, the phylogenetic diversity map, and the global genetic diversity map are fused to obtain a fusion result, wherein the preset fusion formula is:

[0032] D(x,y)=w1·P AOH (x,y)+w2·PD(x,y)+w3·He(x,y)

[0033] Where D(x,y) is the value of the comprehensive diversity index at position (x,y), and P AOH (x,y) represents the species richness at location (x,y), PD(x,y) represents the phylogenetic diversity at location (x,y), He(x,y) represents the genetic diversity at location (x,y), w1 represents the fusion weight of the spatial map of species diversity, w2 represents the fusion weight of the phylogenetic diversity map, and w3 represents the fusion weight of the global genetic diversity map.

[0034] Optionally, in some embodiments, the remote sensing data includes at least one of: high-resolution optical image data, synthetic aperture radar data, lidar data, nighttime light data, terrain data, climate reanalysis data, and future simulation product data;

[0035] The ground-based observation data includes at least one of the following: meteorological station records, flux tower data, ecological station plot monitoring data, near-ground phenological camera time series data, and hyperspectral observation data.

[0036] Optionally, in some embodiments, the statistical characteristics include at least one of the following: annual mean, annual standard deviation, annual coefficient of variation, interannual variation, seasonal variation, and greenness growth period length;

[0037] The frequency characteristics include at least one of high temperature frequency and low temperature frequency.

[0038] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the biodiversity mapping method as described in the above embodiments.

[0039] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the biodiversity mapping method as described in the above embodiments.

[0040] Thus, the present application has at least the following beneficial effects:

[0041] (1) The present application integrates multi-source satellite remote sensing data and ground observation data, covers multiple dimensions of ecological structure, environmental state and biological distribution, and constructs a unified standard data cube.

[0042] (2) The present application introduces multiple types of ecological factors and time series statistical characteristics, which improves the response ability of species and diversity modeling to dynamic environmental changes.

[0043] (3) The present application realizes multi-level spatial mapping of species, phylogeny and genetic diversity by integrating phylogenetic information and genetic sequences, breaking through the limitation of traditional biodiversity mapping only at the species level.

[0044] (4) The present application can identify key ecological regions and diversity hotspots, serve global-scale protection priority delineation and policy decision-making, and has wide scientific value and application potential.

[0045] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0046] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0047] Figure 1 A flowchart of a method for mapping biodiversity according to an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the principle of a method for mapping biodiversity according to an embodiment of the present application;

[0049] Figure 3 A schematic diagram of a device for mapping biodiversity according to an embodiment of the present application;

[0050] Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0052] The biodiversity mapping method, device, electronic equipment and storage medium of the embodiments of the present application are described below with reference to the accompanying drawings. In view of the problem that the related technologies mentioned in the above background art rely on the number of samples and data accuracy, it is difficult to reveal the phylogenetic and genetic level patterns, and it is difficult to meet the needs of global scale decision and management, the present application provides a biodiversity mapping method. In the method, by fusing remote sensing observation data, ecological data and biological information data, a multi-source modeling and spatial expression system for species diversity, phylogenetic diversity and genetic diversity is established. The method can not only depict the current diversity pattern, but also can be linked with climate scenario data to predict future trends, providing a scientific basis for global biodiversity conservation.

[0053] Before introducing the biodiversity mapping method of the embodiments of the present application, the related technical principles are introduced.

[0054] Phylogenetic diversity quantifies the relationship between different species through evolutionary tree structure, and can identify key species and regions with unique evolutionary history. Genetic diversity measures the adaptive potential and ecological restoration capacity of populations through population genetic structure, which is the basis for the stability of biodiversity. Although related biological databases (such as NCBI GenBank (public biological sequence database), Open Tree of Life (open tree of life) and the like) have accumulated a large amount of phylogenetic and gene sequence information, how to effectively fuse these information with spatial environment data and realize large-scale spatial explicit expression still faces the bottleneck of non-uniform methods, low efficiency and difficulty in integration.

[0055] On the other hand, the development of remote sensing technology provides an unprecedented data source for ecosystem monitoring. At present, multi-spectral, hyperspectral, radar, lidar and other multi-source satellite and ground observation systems can obtain large-scale, multi-temporal and high-resolution ecological environment data. With the support of artificial intelligence and high-performance computing, deep fusion of remote sensing and biological data is possible, which is expected to break through the limitations of traditional ecological mapping methods and realize multi-dimensional, multi-scale and multi-class biodiversity information extraction from habitat to genetic level, providing data support for global biodiversity protection strategy and target assessment.

[0056] Therefore, it is urgent to develop a global terrestrial vertebrate and higher plant multi-level diversity mapping method, which can systematically fuse remote sensing observation, ecological classification, biological information and artificial intelligence technology, not only realizing accurate mapping of species distribution and habitat, but also extending to spatial expression at phylogenetic and genetic levels. This method helps to improve the comprehensiveness and scientificity of diversity assessment, promotes the intelligent transformation of global scale biodiversity monitoring, and meets the real needs of international policy for multi-dimensional and multi-scale information support.

[0057] In particular, Figure 1 A flowchart of a biodiversity mapping method provided by an embodiment of the present application.

[0058] As Figure 1 shown, the biodiversity mapping method includes the following steps:

[0059] In step S101, remote sensing observation data, ground-based observation data, and species information data are acquired.

[0060] The remote sensing observation data includes at least one of high-resolution optical image data, synthetic aperture radar data, lidar data, night light data, terrain data, climate reanalysis data, and future simulation product data; and the ground-based observation data includes at least one of weather station record data, flux tower data, ecological station plot monitoring data, near-ground phenology camera time series data, and hyperspectral observation data.

[0061] Specifically, in combination with Figure 2 shown, for acquiring remote sensing observation data, the present application acquires multi-source remote sensing observation data covering ecological elements such as spatial structure, vegetation state, climate environment, and human activities in a global range, mainly including DEM (Digital Elevation Model), SAR (Synthetic Aperture Radar), GEDI (Global Ecosystem Dynamics Investigation), and other spatial structure data, NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), and other vegetation indices provided by MODIS (Moderate Resolution Imaging Spectroradiometer), Sentinel-2, Landsat, and the like, reanalysis climate data such as ERA5 and CHIRPS, future climate scenarios (such as SSP2-4.5, SSP5-8.5) provided by CMIP6, and human activity remote sensing products such as night light and land use change. All data are uniformly projected, spatially resampled, temporally interpolated, and rasterized to form structured and standardized remote sensing data sets.

[0062] For ground-based observation data, this application integrates multiple types of ground-based observation data to enhance the ground-based constraints and ecological interpretability of remote sensing, including conventional meteorological data such as temperature, precipitation, humidity, and wind speed from meteorological stations, and NPP (Net Primary Production) data recorded by the ecological flux station (FLUXNET).

[0063] Data on ecological processes such as net primary productivity (NPMP), carbon exchange, and energy flux; long-term observational data on biomass, cover, and soil moisture from national ecological monitoring stations; plant physiological parameters acquired by SIF tower stations and ground-based hyperspectral instruments; and vegetation phenological images collected by systems such as PhenoCam. All ground-based data, after outlier removal, temporal interpolation, and spatial reconstruction, were matched with remote sensing data in terms of spatial resolution and temporal scale, providing high-precision ground-based observational support for modeling.

[0064] For species information data, occurrence records of target species are extracted from platforms such as GBIF (Global Biodiversity Information Facility) and national species distribution databases, and phylogenetic tree and gene sequence data are obtained from platforms such as NCBI (National Center for Biotechnology Information) and OTOL (Open Tree of Life). Distribution point information is cleaned and spatial accuracy is checked to establish positive and negative sample sets. Occurrence location information of target species is extracted from global or national species observation databases (such as GBIF, national biodiversity monitoring platforms, etc.), and records with abnormal geographical coordinates, missing collection time, or unclear species identification are screened out to establish a high-quality positive sample set. At the same time, pseudo-negative sample point sets are constructed in areas where the species has not been observed according to the principles of spatial uniformity or ecological distance.

[0065] In practice, the first step is to collect multi-source remote sensing and ground-based ecological monitoring data. Remote sensing data includes medium- and high-resolution optical imagery (such as MODIS, Landsat, Sentinel-2), synthetic aperture radar (such as Sentinel-1), lidar (such as GEDI), nighttime light (VIIRS, DMSP), topographic data (such as SRTMDEM), climate reanalysis (such as ERA5), and future simulation products (CMIP6SSP2-4.5, SSP5-8.5). This data undergoes a unified projection transformation P, spatial resampling R, and temporal reconstruction T to generate a standard raster.

[0066] G r =T(R(P(D) r )));

[0067] wherein G r is the standard grid data after projection transformation, spatial resampling and time reconstruction, D r is the original input diversity grid data, P is the projection transformation, R is the spatial resampling, and T is the time reconstruction.

[0068] The ground observation data includes weather station records (temperature, precipitation, wind speed), flux tower data (such as NPP, NEE), ecological station plot monitoring (leaf area index, vegetation coverage), near-ground phenology camera time series (GCC) and hyperspectral observation (such as SIF). The time series data {x t} is subjected to quality control, abnormal values are removed by using standard deviation, and time series interpolation is performed:

[0069]

[0070] wherein x t is the original observation value at time t, x t ' is the sequence value after abnormal value correction, μ is the mean of the sequence, and 3σ is the three times standard deviation of the sequence, which is used to determine whether the data is an abnormal value.

[0071] The species distribution information is obtained from databases such as GBIF, Map of Life, national platform, etc., and observation points that have been species identified, time positioned and coordinate accuracy verified are obtained. After constructing the positive samples, pseudo-negative samples are sampled from the unrecorded area combined with the ecological distance method, so that the positive and negative samples are evenly distributed.

[0072] In step S102, an ecological data cube is constructed based on the remote sensing observation data, the ground observation data and the species information data, and statistical features, frequency features and phenological features are extracted according to the ecological data cube.

[0073] The statistical features include at least one of annual mean, annual standard deviation, annual coefficient of variation, interannual variability, seasonal amplitude and greenness growing season length; and the frequency features include at least one of high temperature frequency and low temperature frequency.

[0074] Specifically, after obtaining the remote sensing observation data, the ground observation data and the species information data, all the data need to be spatially registered, rasterized (such as 1km accuracy), and an integrated ecological data cube is constructed as the basis for diversity modeling.

[0075] The statistical features of the remote sensing and meteorological time series are extracted, such as annual mean, extreme value, standard deviation, coefficient of variation, and frequency features such as extreme high temperature frequency, vegetation fluctuation frequency and growing season length.

[0076] For species distribution and ecological gradient, construct the environmental factor matrix, and conduct correlation and collinearity analysis (such as VIF calculation), combined with principal component analysis (PCA), variable importance analysis (such as random forest) and other methods for multiple rounds of screening, to determine the key environmental factor set for the three types of diversity modeling.

[0077] In actual implementation, remote sensing, ground-based and species data are matched, the grid resolution is unified (such as 500m or 1km), and the research area is cropped to construct an ecological data cube:

[0078] C(x,y,t)={v1(x,y,t),v2(x,y,t),…,v n (x,y,t)};

[0079] Where C(x,y,t) is the ecological data cube at position (x,y) and time t, x is the longitude coordinate, y is the latitude coordinate, t is the time index, v n is the nth ecological variable or observation value at this spatiotemporal position.

[0080] Based on the ecological data cube, statistical, frequency and phenology features are extracted as modeling inputs.

[0081] For continuous variables v(t), the following statistical features are extracted:

[0082] Annual mean:

[0083] Where μ is the annual mean, T is the total observation time length (such as the number of time steps in a year), v(t) is the observation value or variable value at time t, and t is the time index.

[0084] Annual standard deviation:

[0085] Where σ is the annual standard deviation, μ is the annual mean, T is the total observation time length (such as the number of time steps in a year), v(t) is the observation value at time t, and t is the time index.

[0086] Annual coefficient of variation (CV):

[0087] Where CV is the annual coefficient of variation, μ is the annual mean, and σ is the annual standard deviation.

[0088] Annual interannual variation amplitude: ΔY=v(t+1)-v(t)

[0089] Where ΔY is the annual interannual variation amplitude, v(t+1) is the observation value at time t+1, and v(t) is the observation value at time t.

[0090] Seasonal amplitude: S=max(vt ) - min(v t ) ;

[0091] where S is the seasonal amplitude, max(v t ) is the maximum value of v t in a year, and min(v t ) is the minimum value of v t in a year.

[0092] Greenness growing season length: defined as the continuous length of time when v(t) > v (threshold) .

[0093] where v(t) is the observation at time t (e.g., vegetation index), v (threshold) is the threshold value to determine the growing season.

[0094] Frequency features are used to characterize extreme climate disturbances:

[0095] High temperature frequency: F T =∑ t 1[v(t) > T0];

[0096] where F T is the high temperature frequency, v(t) is the meteorological observation at time t (e.g., temperature), and T0 is the high temperature threshold.

[0097] Drought frequency: F D =∑ t 1[v(t) < D0];

[0098] where F D is the drought frequency, v(t) is the meteorological observation at time t (e.g., precipitation or humidity), and D0 is the drought threshold.

[0099] Species distribution information is derived from GBIF and Map of Life. Species distribution information is derived from GBIF and Map of Life. Variance Inflation Factor (VIF) is calculated through Pearson coefficient matrix RRR analysis:

[0100]

[0101] where VIF i is the variance inflation factor of the i-th variable, is the determination coefficient when the i-th variable is the dependent variable and other variables are the independent variables, and i is the variable index.

[0102] Remove collinearity serious (such as VIF> 10) variable. Combine random forest variable importance score (Gini Gain or Mean Decrease Accuracy) and principal component analysis (PCA) dimensionality reduction, screen a robust, informative feature subset.

[0103] In step S103, the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map are constructed based on the statistical features, the frequency features and the phenological features, the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map are fused to obtain a fusion result, high diversity regions and low protection regions are identified according to the fusion result, and the high diversity regions and the low protection regions are evaluated to obtain an evaluation result.

[0104] Optionally, in some embodiments, the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map are constructed based on the statistical features, the frequency features and the phenological features, including: based on the statistical features, the frequency features and the phenological features, a preset maximum entropy model and a species habitat suitability model are adopted, and the species diversity spatial map is obtained based on the species habitat suitability model; based on the statistical features, the frequency features and the phenological features, a phylogenetic diversity index is calculated, and the phylogenetic diversity map is obtained based on the phylogenetic diversity index; based on the statistical features, the frequency features and the phenological features, expected heterozygosity and / or nucleotide diversity are calculated, and the global genetic diversity map is obtained based on the expected heterozygosity and / or nucleotide diversity.

[0105] Specifically, based on high-quality species distribution samples and environmental factors, a preset maximum entropy model MaxEnt and a preset maximum entropy model are used to construct a species habitat suitability model, and a species diversity spatial map (such as AOH map) is generated.

[0106] Based on phylogenetic tree information, combined with species spatial distribution, the Faith phylogenetic diversity index is calculated and mapped to the global scale to form a phylogenetic diversity map.

[0107] Based on species genetic sequence information and spatial distribution, genetic diversity indicators such as expected heterozygosity (He) and nucleotide diversity (π) are calculated, and a global genetic diversity map is constructed by spatial interpolation.

[0108] Based on the above data processing results, the three types of diversity results are fused and superimposed, and high diversity regions and low protection areas are identified by clustering, principal component synthesis or weighted scoring, and the threat level and protection priority of the diversity hot spot region are evaluated in combination with land use, protected area boundaries and climate scenario prediction, to provide decision basis for global biodiversity strategic layout and key ecological region identification.

[0109] In actual implementation process, the embodiment of the application respectively constructs three kinds of diversity mapping model:

[0110] 1. Diversity modeling.

[0111] Based on the constructed sample and variable set, use MaxEnt or random forest model to construct habitat suitability model:

[0112] max p [-∑ x p(x)logp(x)], make

[0113] Wherein, p(x) is the probability distribution of sample point, f j (x) is the jth environmental characteristic function, is the expected value of the characteristic function on the sample point.

[0114] The output suitability probability map P(x,y) is combined with the species habitat threshold θ to generate a binary habitat map:

[0115]

[0116] Wherein, P(x,y) is the suitability probability of position (x,y), θ is the threshold.

[0117] 2. Development diversity modeling.

[0118] Based on the phylogenetic tree TTT, the species set in each spatial unit is mapped to the branch set B(A), and the Faith index is calculated:

[0119] PD A =∑ b∈B(A) L b ;

[0120] Wherein, PD A is the phylogenetic diversity of set A, b is the branch of phylogenetic tree, B(A) is the branch set covered by set A, L b is the length of branch b.

[0121] If you want to emphasize the uniqueness and rarity of the branch, you can introduce weighted phylogenetic diversity:

[0122]

[0123] Wherein, ω is the weighting coefficient, f b is the frequency of branch b.

[0124] 3. Genetic diversity modeling.

[0125] According to the species gene data, the genetic index of the population in each grid is calculated. The expected heterozygosity is mainly used:

[0126]

[0127] wherein He is the expected heterozygosity, is the square of the frequency of the i th allele, and k is the number of allele types.

[0128] Optionally, nucleotide diversity can also be introduced:

[0129]

[0130] wherein π is the nucleotide diversity, d ij is the base difference between the i th and j th sequences, and n is the number of individuals. Kriging or IDW interpolation is used to realize the grid expression.

[0131] Optionally, in some embodiments, the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map are fused to obtain a fusion result, including: based on a preset fusion formula, the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map are fused to obtain a fusion result, wherein the preset fusion formula is:

[0132] D(x,y)=w1·P AOH (x,y)+w2·PD(x,y)+w3·He(x,y)

[0133] wherein D(x,y) is the value of the comprehensive diversity index at position (x,y), P AOH (x,y) is the species richness at position (x,y), PD(x,y) is the phylogenetic diversity at position (x,y), He(x,y) is the genetic diversity at position (x,y), w1 is the fusion weight of the species diversity spatial map, w2 is the fusion weight of the phylogenetic diversity map, and w3 is the fusion weight of the global genetic diversity map.

[0134] After standardizing the three types of diversity maps, they are superimposed to construct a fusion diversity index. The fusion result is divided into zones using the Jenks natural break method, and is superimposed with the existing protected area boundaries to analyze and identify the "high diversity-low protection coverage area". Further combined with the habitat simulation results in the SSP2 / SSP5 scenarios in 2050 and 2100, the suitable habitat change rate is calculated:

[0135]

[0136] wherein ΔS is the suitable grid change rate, S future is the number of suitable grids in the future scenario, and Scurrent is the current suitable grid number.

[0137] And analyze the spatial migration direction, aggregation changes and fragmentation trend of diversity hotspots, to assist in formulating the layout scheme of priority protection areas and identifying key migration channels.

[0138] Therefore, the mapping method of biological diversity proposed in the embodiments of the present application integrates multi-source remote sensing data and ground observation information, combines artificial intelligence and supercomputing acceleration technology, and realizes multi-level spatial mapping of global terrestrial vertebrate and higher plant species diversity, phylogenetic diversity and genetic diversity. The method is suitable for ecological protection evaluation, biodiversity monitoring and policy support scenarios.

[0139] According to the mapping method of biological diversity proposed in the embodiments of the present application, remote sensing observation data, ground observation data and species information data are obtained, an ecological data cube is constructed based on the remote sensing observation data, ground observation data and species information data, statistical features, frequency features and phenological features are extracted according to the ecological data cube, and species diversity spatial maps, phylogenetic diversity maps and global genetic diversity maps are constructed based on the statistical features, frequency features and phenological features. The species diversity spatial maps, phylogenetic diversity maps and global genetic diversity maps are fused to obtain a fusion result, and high diversity areas and low protection areas are identified according to the fusion result, and the high diversity areas and low protection areas are evaluated to obtain an evaluation result. Therefore, the related art relies on the number of samples and data accuracy, which is difficult to reveal the phylogenetic and genetic patterns, and is difficult to meet the needs of global scale decision and management. The present application can integrate multi-source satellite remote sensing and ground data to improve the comprehensiveness and scientificity of biodiversity evaluation.

[0140] Second, the mapping device of biological diversity according to the embodiments of the present application is described with reference to the accompanying drawings.

[0141] Figure 3 is a block schematic diagram of the mapping device of biological diversity of the embodiments of the present application.

[0142] As Figure 3 shown, the mapping device of biological diversity 10 includes an acquisition module 100, a construction module 200 and a mapping module 300.

[0143] The acquisition module 100 is configured to acquire remote sensing observation data, ground observation data and species information data.

[0144] The construction module 200 is configured to construct an ecological data cube based on the remote sensing observation data, ground observation data and species information data, and extract statistical features, frequency features and phenological features according to the ecological data cube.

[0145] The mapping module 300 is configured to construct a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map based on the statistical features, the frequency features and the phenology features, fuse the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain a fusion result, identify a high diversity area and a low protection area according to the fusion result, and evaluate the high diversity area and the low protection area to obtain an evaluation result.

[0146] Optionally, in some embodiments, the mapping module 300 comprises a first construction unit, a second construction unit and a third construction unit.

[0147] The first construction unit is configured to construct a species habitat suitability model based on the statistical features, the frequency features and the phenology features, and using a preset maximum entropy model and a preset random forest model, and obtain the species diversity spatial map based on the species habitat suitability model.

[0148] The second construction unit is configured to calculate a phylogenetic diversity index based on the statistical features, the frequency features and the phenology features, and obtain the phylogenetic diversity map based on the phylogenetic diversity index.

[0149] The third construction unit is configured to calculate an expected heterozygosity and / or a nucleotide diversity based on the statistical features, the frequency features and the phenology features, and obtain the global genetic diversity map based on the expected heterozygosity and / or the nucleotide diversity.

[0150] Optionally, in some embodiments, fusing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain the fusion result comprises: fusing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map based on a preset fusion formula to obtain the fusion result, wherein the preset fusion formula is:

[0151] D(x, y) = w1·P AOH (x, y) + w2·PD(x, y) + w3·He(x, y)

[0152] wherein D(x, y) is a value of a comprehensive diversity index at a position (x, y), P AOH (x, y) is a species richness at the position (x, y), PD(x, y) is a phylogenetic diversity at the position (x, y), He(x, y) is a genetic diversity at the position (x, y), w1 is a fusion weight of the species diversity spatial map, w2 is a fusion weight of the phylogenetic diversity map, and w3 is a fusion weight of the global genetic diversity map.

[0153] Optionally, in some embodiments, the remote sensing observation data comprises at least one of high-resolution optical imagery data, synthetic aperture radar data, lidar data, night light data, terrain data, climate reanalysis data, and future simulation product data; and the ground-based observation data comprises at least one of weather station record data, flux tower data, ecological station plot monitoring data, near-ground phenology camera time series data, and hyperspectral observation data.

[0154] Optionally, in some embodiments, the statistical features comprise at least one of annual mean, annual standard deviation, annual coefficient of variation, inter-annual variability, seasonal amplitude, and greenness growing season length; and the frequency features comprise at least one of high temperature frequency and low temperature frequency.

[0155] It should be noted that the above description of the embodiment of the mapping method of biological diversity also applies to the mapping device of biological diversity of this embodiment, which will not be described here.

[0156] The mapping device of biological diversity provided by the embodiment of the present application, by acquiring remote sensing observation data, ground-based observation data and species information data, constructing an ecological data cube based on the remote sensing observation data, the ground-based observation data and the species information data, extracting statistical features, frequency features and phenological features according to the ecological data cube, constructing a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map based on the statistical features, the frequency features and the phenological features, fusing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain a fusion result, and identifying high diversity areas and low protection areas according to the fusion result and evaluating the high diversity areas and the low protection areas to obtain an evaluation result. Thus, the problem that the related art relies on the number of samples and the accuracy of data, is difficult to reveal the phylogenetic and genetic patterns, and is difficult to meet the needs of global-scale decision-making and management is solved, and the present application can fuse multi-source satellite remote sensing and ground-based data to improve the comprehensiveness and scientificity of diversity evaluation.

[0157] Figure 4 The structure schematic diagram of the electronic device provided by the embodiment of the present application is shown in the figure. The electronic device can include:

[0158] The memory 401, the processor 402, and the computer program stored in the memory 401 and executable on the processor 402.

[0159] The processor 402 executes the program to implement the mapping method of biological diversity provided in the above embodiments.

[0160] Further, the electronic device further includes:

[0161] The communication interface 403 is used for communication between the memory 401 and the processor 402.

[0162] The memory 401 is configured to store a computer program capable of being executed by the processor 402.

[0163] The memory 401 can include a high-speed RAM (Random Access Memory) memory, and can further include a nonvolatile memory such as at least one disk memory.

[0164] If the memory 401, the processor 402 and the communication interface 403 are independently implemented, the communication interface 403, the memory 401 and the processor 402 can be connected with each other through a bus and complete communication between each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 4 In the figure, only one thick line is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0165] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can complete communication between each other through an internal interface.

[0166] The processor 402 can be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0167] The embodiment of the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement the above-mentioned method for mapping biodiversity.

[0168] In the description of the application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the description of the application, the illustrative description of the above terms is not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, different embodiments or examples described in the description of the application and the features of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0169] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0170] Any process or method descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing the specified logical functions or steps, and the preferred embodiments of the application include additional or fewer steps or methods, as appropriate or desired, and that the steps or methods represented in flow charts can be implemented in an order different than those described herein, such that the order of some steps can be different, including use of some steps in reverse order, and some steps can be performed simultaneously or with partial concurrence, particularly when benefit can be gained from doing so. The description herein of certain examples does not preclude additional examples that can be implicit to one of ordinary skill in the art.

[0171] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As in another embodiment, if implemented in hardware, any of the following technologies known in the art or their combinations can be used: discrete logic circuit with logic gate circuit for implementing logical functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array, field programmable gate array, etc.

[0172] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. The program, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0173] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and that changes, modifications, substitutions and variations can be made by those skilled in the art without departing from the scope of the present application.

Claims

1. A method of mapping biodiversity, characterized in that, The method comprises the following steps: obtaining remote sensing observation data, ground observation data and species information data constructing an ecological data cube based on the remote sensing observation data, ground observation data and species information data, and extracting statistical features, frequency features and phenological features from the ecological data cube; constructing a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map based on the statistical features, the frequency features and the phenological features, fusing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain a fusion result, and identifying high diversity areas and low protection areas according to the fusion result and evaluating the high diversity areas and the low protection areas to obtain an evaluation result.

2. The method of claim 1, wherein, The method for constructing a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map based on the statistical features, the frequency features and the phenological features comprises: based on the statistical features, the frequency features and the phenological features, a preset maximum entropy model and a preset random forest model are used to construct a species habitat suitability model, and the species diversity spatial map is obtained based on the species habitat suitability model; based on the statistical features, the frequency features and the phenological features, a phylogenetic diversity index is calculated, and the phylogenetic diversity map is obtained based on the phylogenetic diversity index; based on the statistical features, the frequency features and the phenological features, expected heterozygosity and / or nucleotide diversity are calculated, and the global genetic diversity map is obtained based on the expected heterozygosity and / or the nucleotide diversity.

3. The method of claim 1, wherein, The method for fusing a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map to obtain a fusion result comprises: based on a preset fusion formula, a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map are fused to obtain a fusion result, wherein the preset fusion formula is: D(x, y) = w1 · P AOH (x, y) + w2 · PD(x, y) + w3 · He(x, y) wherein D(x, y) is the value of the integrated diversity index at position (x, y), P AOH (x, y) is the species richness at position (x, y), PD(x, y) is the phylogenetic diversity at position (x, y), He(x, y) is the genetic diversity at position (x, y), w1 is the fusion weight of the species diversity spatial map, w2 is the fusion weight of the phylogenetic diversity map, and w3 is the fusion weight of the global genetic diversity map.

4. The method of claim 1, wherein, The remote sensing observation data comprises at least one of high-resolution optical image data, synthetic aperture radar data, laser radar data, night light data, terrain data, climate reanalysis data and future simulation product data; The ground observation data comprises at least one of weather station record data, flux tower data, ecological station sample plot monitoring data, near-ground phenology camera time series data and hyperspectral observation data.

5. The method of claim 1, wherein, The statistical features comprise at least one of annual mean value, annual standard deviation, annual coefficient of variation, interannual variation amplitude, seasonal amplitude and greenness growing period length. The frequency features comprise at least one of high temperature frequency and low temperature frequency.

6. A biodiversity mapping device, characterized by The method comprises the following steps: an obtaining module for obtaining remote sensing observation data, ground observation data and species information data a constructing module for constructing an ecological data cube based on the remote sensing observation data, ground observation data and species information data, and extracting statistical features, frequency features and phenological features from the ecological data cube; The mapping module is configured to construct a species diversity spatial map, a phylogenetic diversity map and a global genetic diversity map based on the statistical features, the frequency features and the phenological features, fuse the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain a fusion result, identify a high diversity area and a low protection area according to the fusion result, and evaluate the high diversity area and the low protection area to obtain an evaluation result.

7. The apparatus of claim 6, wherein, The mapping module comprises: A first constructing unit configured to construct a species habitat suitability model based on the statistical features, the frequency features and the phenological features, and using a preset maximum entropy model and a preset random forest model, and obtain the species diversity spatial map based on the species habitat suitability model; A second constructing unit configured to calculate a phylogenetic diversity index based on the statistical features, the frequency features and the phenological features, and obtain the phylogenetic diversity map based on the phylogenetic diversity index; A third constructing unit configured to calculate an expected heterozygosity and / or a nucleotide diversity based on the statistical features, the frequency features and the phenological features, and obtain the global genetic diversity map based on the expected heterozygosity and / or the nucleotide diversity.

8. The apparatus of claim 6, wherein, The fusion of the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map to obtain the fusion result comprises: Fusing the species diversity spatial map, the phylogenetic diversity map and the global genetic diversity map based on a preset fusion formula to obtain the fusion result, wherein the preset fusion formula is: D(x, y) = w1 · P AOH (x, y) + w2 · PD(x, y) + w3 · He(x, y) wherein D(x, y) is the value of the integrated diversity index at position (x, y), P AOH (x, y) is the species richness at position (x, y), PD(x, y) is the phylogenetic diversity at position (x, y), He(x, y) is the genetic diversity at position (x, y), w1 is the fusion weight of the species diversity spatial map, w2 is the fusion weight of the phylogenetic diversity map, and w3 is the fusion weight of the global genetic diversity map.

9. An electronic device, comprising: comprises: A memory, a processor and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the mapping method of biological diversity according to any one of claims 1-5.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the mapping method of biological diversity according to any one of claims 1-5.

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