Dynamic monitoring method for spatial distribution of urban biodiversity

Abstract

The invention discloses a dynamic monitoring method for urban biodiversity spatial distribution, which relates to the technical field of biodiversity distribution investigation and comprises the following steps: extracting an urban built-up area boundary by using multi-source data; public big data and an extrapolation method are used for generating an urban area biodiversity spatial distribution map. According to the method, the urban region biodiversity map with fine resolution is generated by adopting the biological distribution record open big data of the network platform in cooperation with the extrapolation method, an analysis framework with universality is developed, the labor and time cost in the stage of obtaining the urban region biodiversity basic data can be reduced, and on the one hand, the method has the advantages of being high in practicability and easy to popularize. By comparing remote sensing image data, data of animals and plants under crown shielding can be obtained, the diversity of species under crowns can be evaluated conveniently, complex information of species composition can be captured, and improvement of adaptability to complex artificial environments and highly fragmented habitat plaques in urban areas is facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of biodiversity distribution survey, and in particular to a dynamic monitoring method for the spatial distribution of urban biodiversity. Background Art

[0002] Urban plant diversity plays an important role in maintaining the dynamic balance of urban ecosystems, improving social service levels, improving the living environment, and promoting the physical and mental health of urban residents. It is also an important indicator for measuring the level of sustainable development of urban environment. Efficient and precise urban biodiversity measurement is the basis for conducting surveys on the spatial distribution of biodiversity in urban areas, dynamically monitoring its changes, and formulating biodiversity planning. It plays an important role in evaluating the level of urban ecosystem construction.

[0003] There are two main methods for evaluating urban biodiversity: one is the traditional method of calculating diversity indexes based on field survey sampling data or literature records; the other is to use remote sensing (RS) and geographic information system (GIS) technology to evaluate vegetation types, coverage, forest degradation and other conditions on a large scale through texture and green volume characteristics.

[0004] In the prior art, a Chinese invention patent with a publication number of CN116502800A discloses a method for evaluating urban biodiversity. The method uses a combination of remote sensing aerial photography technology and manual observation to obtain biological species and number data, and based on this biological annotation feature data, trains various sampling area aerial image feature extraction models to build a regional biodiversity database. This technology can meet the simulation of biodiversity on a larger scale, but the plant species distribution data needs to be obtained through manual field survey sampling sites, which requires a lot of manpower and time costs, and requires investigators to have high professional knowledge. Another Chinese invention patent with a publication number of CN116485275A also proposes a method for evaluating the status of urban biodiversity. It establishes an evaluation data set based on a variety of basic information and data, and comprehensively evaluates urban biodiversity from three aspects: ecological security pattern, species diversity, and biodiversity management. However, in the data set construction stage, this method also relies on traditional data sources such as remote sensing images, field surveys, questionnaires, and government department documents, which has high requirements for data integrity and makes the estimation of biodiversity in urban areas limited to the concept of administrative districts.

[0005] The above analysis of existing technologies shows that the current biodiversity assessment methods for urban areas are still mainly based on traditional field sampling surveys in the basic data acquisition stage, which not only has a certain professional threshold for operators, but also requires a lot of manpower and time. However, due to the particularity of the urban environment, remote sensing data has obvious deficiencies in data integrity and accuracy. For example, since remote sensing data is aerial images, it is impossible to assess species diversity under the canopy and it is difficult to capture complex information about species composition. To this end, we propose a dynamic monitoring method for the spatial distribution of urban biodiversity. Summary of the invention

[0006] The purpose of the present invention is to provide a dynamic monitoring method for the spatial distribution of urban biodiversity to solve the problems that the existing technology proposed in the above background technology requires a lot of manpower and time and is unable to evaluate the species diversity under the canopy and has difficulty in capturing the complex information of species composition.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for dynamic monitoring of the spatial distribution of urban biodiversity, comprising extracting the boundaries of urban built-up areas using multi-source data; generating a spatial distribution map of biodiversity in urban areas using public big data and extrapolation method, wherein generating a spatial distribution map of biodiversity in urban areas comprises the following steps:

[0008] S1: Obtain and screen the distribution data of plants and animals in the target area on the public platform;

[0009] S2: Determine the size of the grid unit in the urban built-up area, divide the urban built-up area into square grids, and identify the grid units for saturated sampling based on the sample coverage of each grid unit calculated after expanding the reference sample size to twice;

[0010] S3: Based on the Hill number index of biodiversity, the abundance and relative richness of species are integrated into a type of diversity measurement index. The estimated biodiversity values ​​of saturated sampling grid cells based on the Hill number framework are obtained by programming in Python. The estimated biodiversity values ​​include species richness, Shannon diversity index, and Simpson diversity index.

[0011] S4: Collect data to build an extrapolation spatial model, evaluate the performance of the extrapolation spatial model, confirm the robustness of the extrapolation spatial model and determine the most reasonable extrapolation method, and extrapolate the diversity of non-saturated sampling units;

[0012] S5: Draw and refine the diversity distribution map, retaining the extrapolated values ​​in unsaturated grid cells and replacing the indicators in saturated grid cells with observed estimates.

[0013] As a further solution of the present invention: the extraction of the urban built-up area boundary includes:

[0014] Obtain remote sensing image data of the target city and perform preprocessing;

[0015] Calculate the initial normalized building index of the target city;

[0016] The night light data is combined with the normalized vegetation index to calculate the urban night light index based on vegetation correction;

[0017] The preliminary normalized building index is corrected using the corrected urban night light index data to obtain an urban night light index based on building correction;

[0018] Using the watershed algorithm, the urban night light index data based on building correction is regarded as the terrain surface. The highland with high pixel brightness is the high value of the regional night light data, and the lowland with low pixel brightness is the low value of the regional night light data. The calculated terrain watershed is the vector boundary of the urban built-up area.

[0019] As a further solution of the present invention: the data for calculating the preliminary normalized building index of the target city is based on the data of the satellite Sentinel-2A.

[0020] As a further solution of the present invention: when acquiring and screening the distribution data of plants and animals in the target area on the public platform, the R interface package of the Global Biodiversity Information Platform is used.

[0021] As a further solution of the present invention: the size of the grid unit in the urban built-up area is based on the resolution of the acquired data, and the acquired data is multi-source data.

[0022] As a further solution of the present invention: the calculation formula of the diversity measurement index is:

[0023]

[0024] In the above formula, i represents the th species, S is the total number of species, and p represents the relative abundance of the th species;

[0025] When q = 0, 0D refers to species richness, which does not take into account the relative abundance of species;

[0026] When q = 1, formula 1 is meaningless, but when q approaches 1, the limit of the formula is the exponent of Shannon entropy, which represents Shannon diversity;

[0027] When q = 1, individuals are counted equally, so the number of species calculated is proportional to their abundance, so 1D can be interpreted as the effective number of common species in the community;

[0028] When q=2, it represents the Simpson index, which means that the effects of other species except dominant species are underestimated, that is, the effective number of dominant species in the community; compared with other diversity measurement indicators, the Hill number is more intuitive and more rigorous in statistics.

[0029] As a further solution of the present invention: when q = 1, the number of species obtained by equally counting individuals is proportional to their abundance, that is, species with more numbers are more likely to be found and more common, where 1D is the effective number of common species in the community;

[0030] When q = 2, it represents the Simpson index, because the relative abundance of each species is squared, and the weight of other species in the calculation is reduced except for the dominant species. This parameter is the effective number of dominant species in the community.

[0031] As a further solution of the present invention: in step S3, the programming formula includes:

[0032] A11_Plants02<-A11_Plants[,colsums(A11_Plants02)>0]

[0033] result<-iNEXT(A11_Plants02,q=c(0,1.2),datatype="abundance"

[0034] #Show my results, here means extracting the sampling adequacy percentage SC value. DataInfo is used to summarize data information, Asy

[0035] result$DataInfo

[0036] result$iNextEst

[0037] result$AsyEst

[0038] ASyESt A1]<-result$AsyESt

[0039] DataInfo A11<-result$DataInfo

[0040] #Calculate the diversity estimate when the sample completeness is unified as 0.9 estimateD(A11 Plants02,datatype"abundance",base_"coverage"level=NULL,conf0.95)

[0041] #Export as SC.CSV file

[0042] write.csv(DataInfo A11,"DataInfo A1l.csv",row.nameS=FALSE)

[0043] #Create a new table variable Species_richness_a11 and add species richness data

[0044] Species_richness_a11<-AsyEst_A1l[seq(1,nrow(ASyEst_A11),by=3),]

[0045] #Export as .CSV file

[0046] write.csv(species_richness_a11."species_richness_a1l.csv", row.names=FALSE).

[0047] As a further solution of the present invention: the collected data includes one or a combination of precipitation data, temperature data, terrain elevation data, actual transpiration data, and soil survey geographic database data.

[0048] As a further solution of the present invention: the extrapolated spatial model includes an ordinary Kriging model and a regression model with covariates.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention uses the open big data of biological distribution records on the network platform in combination with the extrapolation method to generate a fine-resolution urban area biodiversity map, and develops a universal analysis framework, which can reduce the labor and time costs in the stage of obtaining basic data on biodiversity in urban areas. On the other hand, by comparing remote sensing image data, data on animals and plants under the shade of tree canopies can be obtained, which is convenient for evaluating species diversity under the tree canopy and can capture complex information on species composition, which is conducive to improving adaptability to complex artificial environments and highly fragmented habitat patches in urban areas.

[0051] 2. The present invention introduces multi-source data in the process of collecting biological distribution data in urban areas, which can truly reflect the scope of urban built-up areas and densely populated areas, is not restricted by administrative unit boundaries, and is conducive to improving the authenticity and accuracy of biological distribution data in urban areas.

[0052] 3. The present invention improves the resolution of biodiversity distribution maps in urban areas by comprehensively utilizing multi-source data such as night light data and normalized vegetation data. At the same time, by extracting fine vector boundaries of urban built-up areas, it can more realistically reflect the biodiversity distribution characteristics of urban built-up areas and densely populated areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of a method for extracting urban built-up area boundaries according to the present invention;

[0054] Figure 2 Flowchart for mapping the spatial distribution of biodiversity in urban areas for the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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.

[0056] like Figure 1 The flowchart of the method for extracting the boundary of urban built-up areas of the present invention is shown, which provides a dynamic monitoring method for the spatial distribution of urban biodiversity, including the following steps:

[0057] Step 1: Obtain remote sensing data of the target city, including remote sensing image data and night light data, and pre-process the data.

[0058] Among them, the ways to obtain satellite image data include the China Resources Satellite Application Center, the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sciences, the Copernicus Open Access Hub, LAADS, and USGS. The night light data mainly include PMSP-OLS and NPP-VIIRS.

[0059] The remote sensing image data in this embodiment uses Sentinel-2A Level-2A data, and the night light data uses NPP-VIIRS data with a higher resolution guarantee to meet the requirements for identifying the scope of fine built-up areas.

[0060] Data preprocessing includes image clipping, geometric correction, radiation correction and atmospheric correction. The satellite image preprocessing in this embodiment is implemented by ENVI software.

[0061] Step 2: Extract the preliminary normalized building index of the target city based on Sentinel-2A data;

[0062] Through different combinations of the 12 bands contained in the Sentinel-2A data, representative pixels of various types of urban built-up areas are selected, and the band combination with the best land use recognition effect is selected;

[0063] After comparison, this embodiment selects the 8th band and the 11th band to calculate the preliminary normalized building index. The calculation formula of the preliminary normalized building index is as follows:

[0064]

[0065] NDBI is the abbreviation of the Normalized Difference Building Index, S8, S 11 They respectively represent the reflectivity of the 8th and 11th bands in the Sentinel-2A data.

[0066] Step 3: Use the night light data from NPP-VIIRS combined with the normalized vegetation index to calculate the corrected urban night light index based on vegetation correction.

[0067] In this embodiment, MODIS sensor data is used to calculate the normalized vegetation index, and the calculation formula of the corrected urban night light index is as follows:

[0068] VANUI=(1-NDVI)×NTL;

[0069] Among them, VANUI is the English abbreviation symbol for the calibrated urban night light index;

[0070] Step 4: Using VANUI data and NPP-VIIRS data, correct the preliminary normalized building index and calculate the more accurate building-corrected urban night light index BANUI. This index will serve as the main basis for identifying the boundaries of urban built-up areas. The calculation formula of the building-corrected urban night light index BANUI is as follows:

[0071] BANUI=(1+NDBI)×V NTL

[0072] Among them, V NTL Indicates the brightness value of NPP-VIIRS data.

[0073] Step 5: Using the watershed algorithm, the building-corrected urban night light index BANUI data is regarded as the terrain surface to extract the vector boundary of the urban built-up area.

[0074] In this embodiment, in order to remove small patches at the boundary of the built-up area and patches near the smooth aggregation boundary, an adaptive weighted median filtering method is used to remove noise from the BANUI image of the urban night light index based on building correction, and the image is morphologically reconstructed. The local maximum of the reconstructed image is taken as the foreground, and the background is marked by the inverse Euclidean distance method. Finally, the watershed segmentation algorithm is used to extract the vector boundary of the urban built-up area.

[0075] like Figure 2 The flowchart of the present invention for drawing a spatial distribution map of biodiversity in urban areas is shown. The method at this stage mainly includes the following steps:

[0076] Step 6: Use the R interface package rgbif 3.7.9 of the Global Biodiversity Information Platform (GBIF) to obtain and screen the plant and animal distribution data of the target area on the public platform. The target species and public organizations or individuals with sufficient data samples should be selected according to the actual needs of the biodiversity distribution assessment.

[0077] This example takes the mapping of the spatial distribution of vascular plant species diversity as an example, downloads all vascular plant species record data of the target area uploaded by the iNaturalist platform from 2020 to the present, and sets the filtering conditions in the RStudio software as query = "Tracheophyta", rank = "PHYLUM", pred_gte ("year", 2020).

[0078] During the data screening process, we first eliminated the data entries with incomplete species name information, missing latitude and longitude data, or recorded geographical locations falling into the ocean from the downloaded original plant distribution record data. Then, combined with the latitude and longitude values ​​of the species uploaded records, the data was imported into the ArcGIS platform, and finally the records within the urban built-up area were screened out.

[0079] Step 7: Identify the grid cells for saturation sampling in the built-up area; determine the grid cell size of the urban built-up area based on the resolution of various types of data, divide the urban built-up area into square grids, and number them in sequence;

[0080] Connect the grid cells with the selected vascular plant distribution record data according to the spatial position relationship, and create an occurrence matrix for calculating various plants in each grid;

[0081] Using Python programming, the sample integrity is measured by the sample coverage of each grid cell calculated after doubling the reference sample size, and is used to identify grid cells with saturated sampling.

[0082] The size of the urban built-up area grid unit selected in this embodiment is 5km×5km, and it is stipulated that the samples with a sample coverage rate ≥90% are complete, that is, the grid units are identified as saturated sampling.

[0083] Step 8: Calculate biodiversity estimates for saturated sampling grid cells;

[0084] This embodiment uses Python programming to calculate the alpha diversity index based on the Hill number framework, including species richness, Shannon diversity index, and Simpson diversity index. To facilitate the unified comparison of the final calculation results, the sample coverage rate is uniformly set to 90% for diversity estimation.

[0085] Step 9: Diversity extrapolation of unsaturated sampling units. Using precipitation data, temperature data, terrain elevation data, actual evaporation data, and soil survey geographic database data, a variety of diversity extrapolation spatial models are constructed, including ordinary Kriging models and regression models with covariates, and through 10-fold cross-validation, 90% of the training data of each model are randomly selected for model development and best variation map fitting, and the mean absolute error (MAE), relative mean absolute error (RRMSE), root mean square error (RMSE) and relative root mean square error (RRMSE) are calculated to evaluate the performance of each model, confirm the robustness of the model and determine the most reasonable extrapolation method. After comparison, the generalized orthogonal model with ordinary Kriging (GAM OK) was selected as the diversity extrapolation spatial model of the final unsaturated sampling unit.

[0086] Step 10: Draw and refine the diversity distribution map, retaining the extrapolated values ​​in unsaturated grid cells and replacing the indicators in saturated grid cells with observed estimates.

[0087] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0088] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic monitoring method for the spatial distribution of urban biodiversity, It is characterized in that The dynamic monitoring method comprises: Extract urban built-up area boundaries using multi-source data; Generate a spatial distribution map of biodiversity in urban areas by using public big data and extrapolation method, wherein generating a spatial distribution map of biodiversity in urban areas comprises the following steps: S1: Obtain and screen the distribution data of plants and animals in the target area on the public platform; S2: Determine the size of the grid unit in the urban built-up area, divide the urban built-up area into square grids, and identify the grid units for saturated sampling based on the sample coverage of each grid unit calculated after expanding the reference sample size to twice; S3: Based on the Hill number index of biodiversity, the abundance and relative richness of species are integrated into a type of diversity measurement index, and the estimated biodiversity of saturated sampling grid cells based on the Hill number framework is obtained by programming in Python; S4: Collect data to build an extrapolation spatial model, evaluate the performance of the extrapolation spatial model, confirm the robustness of the extrapolation spatial model and determine the most reasonable extrapolation method, and extrapolate the diversity of non-saturated sampling units; S5: Draw and refine the diversity distribution map, retaining the extrapolated values ​​in unsaturated grid cells and replacing the indicators in saturated grid cells with observed estimates.

2. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 1, Features: The extraction of the urban built-up area boundary includes: Obtain remote sensing image data of the target city and perform preprocessing; Calculate the initial normalized building index of the target city; The night light data is combined with the normalized vegetation index to calculate the urban night light index based on vegetation correction; The preliminary normalized building index is corrected using the corrected urban night light index data to obtain an urban night light index based on building correction; Using the watershed algorithm, the urban night light index data based on building correction is regarded as the terrain surface. The highland with high pixel brightness is the high value of the regional night light data, and the lowland with low pixel brightness is the low value of the regional night light data. The calculated terrain watershed is the vector boundary of the urban built-up area.

3. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 2, Features: The data for calculating the preliminary normalized building index of the target cities are based on data from the satellite Sentinel-2A.

4. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 1, Features: The R interface package of the Global Biodiversity Information Platform was used to obtain and screen the distribution data of plants and animals in the target area on the public platform.

5. The method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 1, Features: The size of the grid unit in the urban built-up area is based on the resolution of the acquired data, which is multi-source data.

6. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 1, Features: The calculation formula of the diversity measurement index is: In the above formula, i represents the th species, S is the total number of species, and p represents the relative abundance of the th species; When q = 0, 0D refers to species richness, which does not take into account the relative abundance of species; When q = 1, formula 1 is meaningless, but when q approaches 1, the limit of the formula is the exponent of Shannon entropy, which represents Shannon diversity; When q = 1, individuals are counted equally, so the number of species calculated is proportional to their abundance, so 1D can be interpreted as the effective number of common species in the community; When q=2, it represents the Simpson index, which is the effective number of dominant species in the community.

7. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 6, Features: When q = 1, the number of species obtained by equally counting individuals is proportional to their abundance, where 1D is the effective number of common species in the community; When q=2, it represents the Simpson index, which is the effective number of dominant species in the community.

8. The method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 1, Features: In step S3, the programming includes: A11_Plants02<-A11_Plants[,colsums(A11_Plants02)>0] result<-iNEXT(A11_Plants02,q=c(0,1.2),datatype="abundance" #Show my results, here means extracting the sampling adequacy percentage SC value. DataInfo is used to summarize data information, Asy result$DataInfo result$iNextEst result$AsyEst ASyESt A1]<-result$AsyESt DataInfo A11<-result$DataInfo #Calculate the diversity estimate when the sample completeness is unified as 0.9 estimateD(A11 Plants02,datatype"abundance",base_"coverage"level=NULL,conf0.95) #Export as SC.CSV file write.csv(DataInfo A11,"DataInfo A1l.csv",row.nameS=FALSE) #Create a new table variable Species_richness_a11 and add species richness data Species_richness_a11<-AsyEst_A1l[seq(1,nrow(ASyEst_A11),by=3),] #Export as .CSV file write.csv(species_richness_a11."species_richness_a1l.csv", row.names=FALSE).

9. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 1, Features: The collected data includes one or a combination of precipitation data, temperature data, terrain elevation data, actual evaporation data, and soil survey geographic database data.

10. A method for dynamic monitoring of spatial distribution of urban biodiversity according to claim 9, Features: The extrapolated spatial models include ordinary Kriging models and regression models with covariates, and the biodiversity estimates include species richness, Shannon diversity index, and Simpson diversity index.

Images