A multi-temporal lake wetland waterbird community diversity inversion method and system

Abstract

The invention relates to a multi-temporal lake wetland waterbird community diversity inversion method and system. According to the waterbird community diversity index of all monitoring points in each monitoring month, the monitored lake wetland is spatially gridded by using the nearest neighbor spatial interpolation method to obtain the waterbird community diversity index grid image of the monitored lake wetland in each monitoring month, and the optimal spatial capture fishing net of the monitored lake wetland is constructed to comprehensively link the wetland waterbird diversity characteristics of similar spatial areas, and then calculate the average value of the waterbird community diversity index of all grids within each grid falling into the optimal spatial capture fishing net, and convert the average value into grid data as the waterbird community diversity index of each grid of the monitored lake wetland in each monitoring month, and finally form the waterbird community diversity index fishing net image of the monitored lake wetland in each monitoring month during the monitoring period, so as to realize the dynamic inversion of the multi-temporal lake wetland wintering waterbird community diversity.

Description

Technical Field

[0001] The present invention relates to the field of wildlife ecology, and in particular to a multi-temporal lake wetland waterbird community diversity inversion method and system. Background Art

[0002] At present, the existing methods for evaluating the diversity dynamics of wintering waterbird communities in lake wetlands are mainly based on the sampling method of sample point surveys and the transect method of line surveys.

[0003] The sampling method is to randomly select several plots in the habitat of the surveyed population, calculate the population density of each plot by counting the number of individuals in each plot, and take the average of the population density of all plots as the population density. In the sampling process, the probability of each individual in the population being selected should be equal as much as possible. At the same time, the sampling method requires that the selected individuals do not involve other individuals, satisfying random independent sampling, that is, random sampling or simple random sampling. This method has a certain degree of subjectivity and is likely to reduce the reliability of the survey results, such as the invention patent with patent number CN 105493858 A.

[0004] The transect method mainly lays out transects at the established survey points according to the habitat type and terrain, and the transects do not overlap each other; the transect length is preferably 1 to 3 km. Use telescopes, digital video cameras, digital cameras, etc. to observe birds within about 200m on both sides of the transect, supplemented by bird calls, flight postures, ecological habits and feathers. Carefully record the name, number and distance from the center line of the birds found, and use the track recorder to record the latitude and longitude, altitude, habitat, transect length and track of the bird species discovery point. If no birds are observed, but the birds can be heard, use a recorder to record their calls as a basis for identifying species. Similar to the transect method, the transect method also has the uncertainty of the survey points, and the results are accidental and discontinuous in time and space. In summary, although the above two biodiversity survey methods can complete the survey of biodiversity in a certain area, due to the strong migration of water birds, the selection of monitoring points is usually random and cannot reflect the dynamic changes in vegetation diversity in the region on a time scale. In addition, the sampling method and the transect line method can only monitor the types and numbers of waterbirds at limited points, so it is impossible to continuously monitor the distribution of waterbird diversity within the regional spatial scope. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-temporal lake wetland waterbird community diversity inversion method and system, so as to realize the dynamic inversion of the multi-temporal lake wetland overwintering waterbird community diversity.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A multi-temporal lake wetland waterbird community diversity inversion method, the method comprising:

[0008] Obtaining water bird community monitoring data at each monitoring point set up in the monitored lake wetland during the monitoring period; the monitoring data includes water bird species and population numbers;

[0009] Calculate the waterbird community diversity index of each monitoring point in each monitoring month during the monitoring period based on the waterbird community monitoring data;

[0010] According to the diversity index of water bird communities at all monitoring points in each monitoring month, the nearest neighbor spatial interpolation method was used to perform spatial gridding on the monitored lake wetlands to obtain the grid images of water bird community diversity index of the monitored lake wetlands in each monitoring month.

[0011] According to the longitude and latitude of the monitoring points arranged in the monitored lake wetland, taking the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization goal, the optimal spatial capture fishing net of the monitored lake wetland is constructed in the water bird community diversity index grid image;

[0012] The average value of the water bird community diversity index of all grids within each grid that falls into the optimal spatial capture fishing net is calculated, and the average value is converted into raster data as the water bird community diversity index of each grid of the monitored lake wetland in each monitoring month, ultimately forming a fishing net image of the water bird community diversity index of the monitored lake wetland in each monitoring month during the monitoring period.

[0013] Optionally, the acquisition of water bird community monitoring data at each monitoring point deployed in the monitored lake wetland during the monitoring period specifically includes:

[0014] According to the distribution characteristics of waterbirds in each monitoring month during the monitoring period, the concentrated distribution points of waterbirds are determined as the monitoring points of the monitored lakes and wetlands in each monitoring month;

[0015] Telescopes are used to monitor and record the water bird community monitoring data at each monitoring point set up in the monitored lake wetlands every monitoring month.

[0016] Optionally, based on the water bird community monitoring data, the water bird community diversity index of each monitoring point in each monitoring month during the monitoring period is calculated, specifically including:

[0017] According to the water bird species and population number of each monitoring point in each monitoring month, the formula The Shannon-Wiener diversity index of each monitoring point in each monitoring month was calculated; mn is the Shannon-Wiener diversity index of the mth monitoring point in the nth monitoring month, is the proportion of individuals of the i-th species in the total number at the m-th monitoring point in the n-th monitoring month;

[0018] According to the Shannon-Wiener diversity index and the number of waterbird species at each monitoring point in each monitoring month, the formula Calculate the Pielou evenness index of each monitoring point in each monitoring month; where P mn is the Pielou uniformity index of the mth monitoring point in the nth monitoring month, L mn is the total number of species in the community at the mth monitoring point in the nth monitoring month;

[0019] According to the water bird species and population number of each monitoring point in each monitoring month, the formula Calculate the Simpson community dominance index for each monitoring point in each monitoring month; where S mn is the Simpson community dominance index of the mth monitoring point in the nth monitoring month, is the number of species i at the mth monitoring point in the nth monitoring month, N mn is the number of all species in the community at the mth monitoring point in the nth monitoring month.

[0020] Optionally, the method of arranging the monitoring points according to the longitude and latitude of the monitored lake wetland and taking the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization target, constructing the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index grid image, specifically includes:

[0021] Determine multiple values ​​of the side length of the fishing net grid according to the grid resolution of the water bird community diversity index grid image;

[0022] According to the latitude and longitude of the monitoring points arranged in the monitored lake wetland, the formula Get the spatial uniformity index when the side length of the fishing net grid is equal to each side length value; where C k N is the spatial uniformity index when the side length of the fishing net grid is k; k1 、N kx 、N kn are the number of monitoring points contained in the 1st, xth, and nth fishing net grids containing water bird monitoring points when the side length of the fishing net grid is k; n is the total number of fishing net grids containing monitoring points;

[0023] The side length value corresponding to the minimum value of all spatial uniformity indexes is used as the most suitable fishing net grid side length, and the optimal spatial capture fishing net of the monitored lake wetland is constructed in the water bird community diversity index grid image based on the most suitable fishing net grid side length.

[0024] A multi-temporal lake wetland waterbird community diversity inversion system, the system comprising:

[0025] A water bird colony monitoring data acquisition module is used to obtain water bird colony monitoring data at each monitoring point arranged in the monitored lake wetland during the monitoring period; the monitoring data includes water bird species and population numbers;

[0026] A water bird community diversity index calculation module is used to calculate the water bird community diversity index of each monitoring point in each monitoring month during the monitoring period according to the water bird community monitoring data;

[0027] The water bird community diversity index grid image acquisition module is used to perform spatial grid processing on the monitored lake wetlands based on the water bird community diversity index of all monitoring points in each monitoring month using the nearest neighbor spatial interpolation method to obtain the water bird community diversity index grid image of the monitored lake wetlands in each monitoring month;

[0028] The optimal spatial capture fishing net construction module is used to construct the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index grid image according to the longitude and latitude of the monitoring points arranged in the monitored lake wetland, taking the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization target;

[0029] The water bird community diversity index fishing net image formation module is used to calculate the average value of the water bird community diversity index of all grids within each grid that falls into the optimal spatial capture fishing net, and convert the average value into raster data as the water bird community diversity index of each grid of the monitored lake wetland in each monitoring month, and finally form the water bird community diversity index fishing net image of the monitored lake wetland in each monitoring month during the monitoring period.

[0030] Optionally, the water bird colony monitoring data acquisition module specifically includes:

[0031] The monitoring point determination submodule is used to determine the concentrated distribution points of waterbirds as the monitoring points of the monitored lake wetlands in each monitoring month according to the distribution characteristics of waterbirds in each monitoring month during the monitoring period;

[0032] The water bird colony monitoring data recording submodule is used to use a telescope to monitor and record the water bird colony monitoring data of each monitoring point deployed in the monitored lake wetland every monitoring month.

[0033] Optionally, the water bird community diversity index calculation module specifically includes:

[0034] The Shannon-Wiener diversity index calculation submodule is used to calculate the number of waterbird species and populations at each monitoring point in each monitoring month using the formula The Shannon-Wiener diversity index of each monitoring point in each monitoring month was calculated; mn is the Shannon-Wiener diversity index of the mth monitoring point in the nth monitoring month, is the proportion of individuals of the i-th species in the total number at the m-th monitoring point in the n-th monitoring month;

[0035] The Pielou evenness index calculation submodule is used to calculate the Pielou evenness index based on the Shannon-Wiener diversity index and the number of waterbird species at each monitoring point in each monitoring month using the formula Calculate the Pielou evenness index of each monitoring point in each monitoring month; where P mn is the Pielou uniformity index of the mth monitoring point in the nth monitoring month, L mn is the total number of species in the community at the mth monitoring point in the nth monitoring month;

[0036] The Simpson colony dominance index calculation submodule is used to calculate the waterbird species and population size at each monitoring point in each monitoring month using the formula Calculate the Simpson community dominance index for each monitoring point in each monitoring month; where S mn is the Simpson community dominance index of the mth monitoring point in the nth monitoring month, is the number of species i at the mth monitoring point in the nth monitoring month, N mn is the number of all species in the community at the mth monitoring point in the nth monitoring month.

[0037] Optionally, the optimal space capture fishing net construction module specifically includes:

[0038] The side length determination submodule is used to determine multiple side length values ​​of the fishing net grid side length according to the grid resolution of the water bird community diversity index grid image;

[0039] The spatial uniformity index acquisition submodule is used to obtain the spatial uniformity index according to the latitude and longitude of the monitoring points arranged in the monitored lake wetland, using the formula Get the spatial uniformity index when the side length of the fishing net grid is equal to each side length value; where C k N is the spatial uniformity index when the side length of the fishing net grid is k; k1 、N kx 、N kn are the number of monitoring points contained in the 1st, xth, and nth fishing net grids containing water bird monitoring points when the side length of the fishing net grid is k; n is the total number of fishing net grids containing monitoring points;

[0040] The optimal spatial capture fishing net construction submodule is used to take the side length value corresponding to the minimum value of all spatial uniformity indexes as the most suitable fishing net grid side length, and construct the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index grid image according to the most suitable fishing net grid side length.

[0041] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0042] The invention discloses a multi-temporal lake wetland waterbird community diversity inversion method and system. According to the waterbird community diversity index of all monitoring points in each monitoring month, the monitored lake wetland is spatially gridded by using the nearest neighbor spatial interpolation method to obtain the waterbird community diversity index grid image of the monitored lake wetland in each monitoring month, and the optimal spatial capture fishing net of the monitored lake wetland is constructed to comprehensively link the wetland waterbird diversity characteristics of similar spatial areas, and then calculate the average value of the waterbird community diversity index of all grids within each grid falling into the optimal spatial capture fishing net, and convert the average value into grid data as the waterbird community diversity index of each grid of the monitored lake wetland in each monitoring month, and finally form the waterbird community diversity index fishing net image of the monitored lake wetland in each monitoring month during the monitoring period, thereby realizing the dynamic inversion of the multi-temporal lake wetland wintering waterbird community diversity. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 A flow chart of a multi-temporal lake wetland waterbird community diversity inversion method provided by the present invention;

[0045] Figure 2 A schematic diagram of a multi-temporal lake wetland waterbird community diversity inversion method provided by the present invention;

[0046] Figure 3 A spatial distribution map of the initial monitoring points of Caizi Lake provided in an embodiment of the present invention;

[0047] Figure 4 A distribution map of actual monitoring points of Caizi Lake provided by an embodiment of the present invention; Figure 4 (a) is the actual monitoring point distribution map from 2018 to 2019. Figure 4 (b) is the actual monitoring point distribution map from 2019 to 2020;

[0048] Figure 5 The spatial interpolation result of the Shannon-Wiener diversity index IDW of Caizi Lake water birds provided in the embodiment of the present invention; Figure 5 (a) is the distribution map of the Shannon-Wiener diversity index of waterbirds. Figure 5 (b) is the spatial interpolation result of the Shannon-Wiener diversity index IDW of waterbirds;

[0049] Figure 6 The most suitable spatial capture fishing net diagram provided by the embodiment of the present invention;

[0050] Figure 7 A graph showing changes in the Shannon-Wiener index of water bird community diversity during the wintering period in 2018-2019 and 2019-2020 provided in an embodiment of the present invention; Figure 7 (A) is the distribution map of the Shannon-Wiener index of waterbird community diversity in October 2018. Figure 7 (B) is the distribution map of the Shannon-Wiener index of waterbird community diversity in November 2018. Figure 7 (C) is the distribution map of the Shannon-Wiener index of waterbird community diversity in December 2018. Figure 7 (D) is the distribution map of the Shannon-Wiener index of waterbird community diversity in January 2019. Figure 7 (E) is the distribution map of the Shannon-Wiener index of waterbird community diversity in October 2019. Figure 7 (F) is the distribution map of the Shannon-Wiener index of waterbird community diversity in November 2019. Figure 7 (G) is the distribution map of the Shannon-Wiener index of waterbird community diversity in December 2019. Figure 7 (H) is the distribution map of the Shannon-Wiener index of waterbird community diversity in January 2020. DETAILED DESCRIPTION

[0051] 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.

[0052] The purpose of the present invention is to provide a multi-temporal lake wetland waterbird community diversity inversion method and system, so as to realize the dynamic inversion of the multi-temporal lake wetland overwintering waterbird community diversity.

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] The present invention provides a multi-temporal lake wetland waterbird community diversity inversion method. Figure 1 As shown, the method includes:

[0055] Step 1: Obtain water bird community monitoring data at each monitoring point deployed in the monitored lake wetland during the monitoring period; the monitoring data include water bird species and population numbers.

[0056] Specifically include:

[0057] According to the distribution characteristics of waterbirds in each monitoring month during the monitoring period, the concentrated distribution points of waterbirds are determined as the monitoring points of the lakes and wetlands monitored in each monitoring month;

[0058] Telescopes are used to monitor and record the water bird community monitoring data at each monitoring point set up in the monitored lake wetlands every monitoring month.

[0059] Step 2: Calculate the waterbird community diversity index for each monitoring point in each monitoring month during the monitoring period based on the waterbird community monitoring data.

[0060] Specifically include:

[0061] According to the water bird species and population number of each monitoring point in each monitoring month, the formula The Shannon-Wiener diversity index of each monitoring point in each monitoring month was calculated; mn is the Shannon-Wiener diversity index of the mth monitoring point in the nth monitoring month, is the proportion of individuals of the i-th species in the total number at the m-th monitoring point in the n-th monitoring month;

[0062] According to the Shannon-Wiener diversity index and the number of waterbird species at each monitoring point in each monitoring month, the formula Calculate the Pielou evenness index of each monitoring point in each monitoring month; where P mn is the Pielou uniformity index of the mth monitoring point in the nth monitoring month, L mn is the total number of species in the community at the mth monitoring point in the nth monitoring month;

[0063] According to the water bird species and population number of each monitoring point in each monitoring month, the formula Calculate the Simpson community dominance index for each monitoring point in each monitoring month; where S mn is the Simpson community dominance index of the mth monitoring point in the nth monitoring month, is the number of species i at the mth monitoring point in the nth monitoring month, N mn is the number of all species in the community at the mth monitoring point in the nth monitoring month.

[0064] Step 3: Based on the water bird community diversity indicators of all monitoring points in each monitoring month, the nearest neighbor spatial interpolation method is used to perform spatial rasterization processing on the monitored lake wetlands to obtain the water bird community diversity indicator raster image of the monitored lake wetlands in each monitoring month.

[0065] Step 4: Based on the longitude and latitude of the monitoring points arranged in the monitored lake wetland, the maximum uniformity of the distribution of each monitoring point in the fishing net is taken as the optimization goal, and the optimal spatial capture fishing net of the monitored lake wetland is constructed in the water bird community diversity index raster image.

[0066] Specifically include:

[0067] Determine multiple values ​​of the side length of the fishing net grid according to the grid resolution of the water bird community diversity index grid image;

[0068] According to the latitude and longitude of the monitoring points arranged in the monitored lake wetland, the formula Get the spatial uniformity index when the side length of the fishing net grid is equal to each side length value; where C k N is the spatial uniformity index when the side length of the fishing net grid is k; k1 、N kx 、N kn are the number of monitoring points contained in the 1st, xth, and nth fishing net grids containing water bird monitoring points when the side length of the fishing net grid is k; n is the total number of fishing net grids containing monitoring points;

[0069] The side length corresponding to the minimum value of all spatial uniformity indices is taken as the most suitable side length of the fishing net grid, and the optimal spatial capture fishing net of the monitored lake wetland is constructed in the water bird community diversity index raster image based on the most suitable side length of the fishing net grid.

[0070] Step 5, calculate the average value of the water bird community diversity index of all grids within each grid that falls into the optimal spatial capture fishing net, and convert the average value into raster data as the water bird community diversity index of each grid of the monitored lake wetland in each monitoring month, and finally form a fishing net image of the water bird community diversity index of the monitored lake wetland in each monitoring month during the monitoring period.

[0071] Reference Figure 2 The specific implementation process of the present invention is as follows:

[0072] 1. Acquisition of geographical data of multi-temporal wetland waterbird communities

[0073] Taking the northernmost geographical point of the lake wetland as the starting point, wintering waterbird monitoring points are set up at intervals of 1km from north to south. With the aid of latitude and longitude grid units and wetland landscape units, 8x binoculars and 20-60x monoculars are used to carry out wintering waterbird monitoring in lake wetlands within the target lake range. It is worth noting that due to the influence of lake water level fluctuations and the uncertainty of wintering waterbird distribution, there is a certain deviation between the actual monitoring points and the initial layout of the sample points, so the specific monitoring points are mainly based on the actual monitoring point layout. The monitoring is carried out by human observation. The monitoring content (human observation) includes basic information such as the latitude and longitude of the monitoring point, waterbird species, and population size. The annual monitoring range generally covers the wintering period (October to March of the following year), the monitoring frequency shall not be less than the month, and the observation year shall not be less than 2 years. The monitoring range needs to cover the entire target lake.

[0074] 2. Calculation of diversity index of wetland waterbird communities

[0075] Based on the basic database of monitored water bird communities, the diversity index of water bird communities is calculated for each monitoring point. The bird community diversity index includes the Shannon-Wiener diversity index (H), the Simpson community dominance index (S) and the Pielou evenness index (P). The algorithms for H, P and S are as follows:

[0076]

[0077]

[0078]

[0079] Then, the water bird community diversity index at the site scale was spatially gridded based on the nearest neighbor spatial interpolation method. Among them, the nearest neighbor spatial interpolation method is a conventional calculation method for spatial point data to be spatially gridded. It is widely used by domestic and foreign scholars and institutions in research related to spatial gridding. The target grid resolution can be set according to research needs, but the interpolation resolution is not greater than the closest distance between two monitoring points. The interpolation space range is the circumscribed rectangle that can cover all monitoring points.

[0080] 3. Construction of the fishing net most suitable for space capture

[0081] In order to ensure that the water bird monitoring points in different periods can be evenly distributed in each fishing net to the maximum extent, the monitoring points of each monitoring month were loaded into ArcMap10.5. The square spatial capture fishing nets with different side lengths were initially set in the experiment, and their side lengths were not less than 2 times the IDW (Inverse Distance Weighted) interpolation grid resolution (R) and not greater than 50 times the grid resolution. The square fishing nets with different side lengths were set with 2 times the pixel side length resolution as the initial side length and 1 times as the step length. The spatial uniformity index C was constructed. k' Used to describe the uniformity of the distribution of each monitoring station in the fishing net C k' , the algorithm is as follows:

[0082]

[0083] k' is the ratio of the length of the fishing net to the IDW interpolation resolution; N k'x is the number of monitoring points contained in the xth fishing net containing water bird monitoring points when the side length of the fishing net is k' times the IDW interpolation grid; n is the total number of fishing nets containing monitoring points.

[0084] When C k' When it is the global minimum value, it is recorded as C ks , the corresponding fishing net side length is the most suitable fishing net side length, and the corresponding side length is R s The calculation method is as follows:

[0085] R s =k S R

[0086] R s is the optimal fishing net side length, k s To adapt to the spatial index C ks The ratio of the corresponding fishing net side length to the IDW interpolation resolution; R is the IDW interpolation spatial resolution.

[0087] IV. Statistics on diversity of waterbird communities in fishing net-chasing wetlands

[0088] In finding the most suitable side length R s Then, a rectangle with a side length of R is constructed with the research area as the boundary. s Fishing nets.

[0089] The average values ​​of wetland waterbird community diversity information (H, S, P) corresponding to all grids whose central longitude and latitude of the IDW interpolation grid falls within the fishing net are calculated for each fishing net.

[0090] And convert the vector data of fishing net statistics into a vector with a side length of R s The raster data is a high-resolution wetland waterbird community diversity index that is continuous in both time and space.

[0091] The final result of the assessment is waterbird diversity.

[0092] The present invention solves the problem of being unable to dynamically evaluate the diversity of multi-temporal waterbird communities in situ, and helps to make reasonable suggestions for the protection of wintering waterbirds and their habitats in lake wetlands at multiple temporal and spatial scales.

[0093] Taking Caizi Lake as a typical study area, this paper provides a method to study the dynamic changes of the diversity of wintering waterbird communities in Caizi Lake wetland.

[0094] Caizi Lake is an important wintering habitat and concentrated gathering area for migratory birds in the middle and lower reaches of the Yangtze River. There are many key protected bird species distributed in the lake area. It is one of the important wintering and stopover sites for East Asian-Australian migratory birds such as white-headed cranes, oriental white storks, bean geese and little swans, and is of great significance to the protection of ecosystems and biodiversity.

[0095] In the first step, a water bird monitoring point was initially set up every 1 km from north to south in the entire Caizi Lake (the same as the experimental research scope), with a total of 17 monitoring points ( Figure 3 ). With the aid of latitude and longitude grid units and wetland landscape units, wetland waterbird monitoring is carried out throughout the entire Caizi Lake. When there is a concentrated distribution of waterbirds at the initial preset monitoring point, this point is the monitoring point. If there is no concentrated distribution of waterbirds, drive to the area with concentrated distribution of waterbirds for monitoring and record the monitoring point

[0096] The location of the monitoring points was then adjusted according to the actual distribution of waterbirds, and basic information such as the latitude and longitude of the waterbird colony monitoring points, waterbird species, and population size were recorded. The monitoring period was from October 2018 to January 2019, and from October 2019 to January 2020. The actual number of monitoring points may differ from the initial set points due to water level changes and the randomness of bird habitats. The actual distribution of the monitoring points this time is as follows: Figure 4 shown.

[0097] In the second step, based on the basic information of the monitored waterbird communities, the waterbird diversity index was calculated for each monitoring point in the monitored month, including the Shannon-Wiener diversity index (H), the Simpson community dominance (S) index, and the Pielou evenness index (P).

[0098] In the third step, the IDW interpolation module in ArcGIS 10.5 was used to convert the monitored waterbird diversity point cloud information into a raster image with a resolution of 0.001°. The interpolation range is the circumscribed rectangle that can cover the Caizi Lake area and all monitoring points. The results are taken as an example of the Shannon-Wiener diversity index of waterbirds in October 2018 ( Figure 5 ).

[0099] In the fourth step, with 0.002° as the initial side length, square space capture nets with different side lengths were set, and the most suitable spatial index C was constructed to characterize the uniformity of the distribution of each monitoring station in the fishing net. Finally, when the side length reached 0.01°, the most suitable spatial index C reached the global minimum, and finally 0.01° was selected as the most suitable side length to construct the fishing net. The results are as follows: Figure 6 shown.

[0100] The fifth step was to construct a fishing net with a side length of 0.01° based on the Create Fishnet module of ArcGIS10.5. Then, the weighted average of the IDW interpolation points falling within the fishing net was calculated based on the diversity information (H, S, P) of the wetland waterbird community, and the vector data of the fishing net statistics was converted into a high-resolution spatiotemporal dynamic change map of waterbirds with a side length of 0.01°. Figure 7 The data shows the changes in the Shannon-Wiener index of waterbird community diversity in Caizi Lake during the wintering period in 2018-2019 and 2019-2020.

[0101] The present invention is a method for inverting the diversity of wetland waterbird communities with high resolution. It mainly constructs the most suitable spatial capture fishing net to comprehensively link the wetland waterbird diversity characteristics of similar spatial areas, thereby providing a scientific technical method for wide-range comparative evaluation of fine-scale waterbird diversity characteristics.

[0102] The present invention also provides a multi-temporal lake wetland waterbird community diversity inversion system, the system comprising:

[0103] The water bird colony monitoring data acquisition module is used to obtain the water bird colony monitoring data of each monitoring point arranged in the monitored lake wetland during the monitoring period; the monitoring data includes water bird species and population numbers;

[0104] The water bird community diversity index calculation module is used to calculate the water bird community diversity index of each monitoring point in each monitoring month during the monitoring period based on the water bird community monitoring data;

[0105] The water bird community diversity index grid image acquisition module is used to perform spatial grid processing on the monitored lake wetlands based on the water bird community diversity index of all monitoring points in each monitoring month using the nearest neighbor spatial interpolation method to obtain the water bird community diversity index grid image of the monitored lake wetlands in each monitoring month;

[0106] The optimal spatial capture fishing net construction module is used to construct the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index grid image according to the longitude and latitude of the monitoring points arranged in the monitored lake wetland, with the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization goal;

[0107] The water bird community diversity index fishing net image formation module is used to calculate the average value of the water bird community diversity index of all grids within each grid that falls into the optimal spatial capture fishing net, and convert the average value into raster data as the water bird community diversity index of each grid of the monitored lake wetland in each monitoring month, and finally form the water bird community diversity index fishing net image of the monitored lake wetland in each monitoring month during the monitoring period.

[0108] The water bird community monitoring data acquisition module includes:

[0109] The monitoring point determination submodule is used to determine the concentrated distribution points of waterbirds as the monitoring points of the lake wetlands monitored in each monitoring month according to the distribution characteristics of waterbirds in each monitoring month during the monitoring period;

[0110] The water bird colony monitoring data recording submodule is used to use a telescope to monitor and record the water bird colony monitoring data of each monitoring point deployed in the monitored lake wetland every monitoring month.

[0111] The water bird community diversity index calculation module includes:

[0112] The Shannon-Wiener diversity index calculation submodule is used to calculate the number of waterbird species and populations at each monitoring point in each monitoring month using the formula The Shannon-Wiener diversity index of each monitoring point in each monitoring month was calculated; mn is the Shannon-Wiener diversity index of the mth monitoring point in the nth monitoring month, Z i mn is the proportion of individuals of the i-th species in the total number at the m-th monitoring point in the n-th monitoring month;

[0113] The Pielou evenness index calculation submodule is used to calculate the number of waterbirds at each monitoring point in each monitoring month according to the Shannon-Wiener diversity index. Calculate the Pielou evenness index of each monitoring point in each monitoring month; where P mn is the Pielou uniformity index of the mth monitoring point in the nth monitoring month, L mn is the total number of species in the community at the mth monitoring point in the nth monitoring month;

[0114] The Simpson colony dominance index calculation submodule is used to calculate the waterbird species and population size at each monitoring point in each monitoring month using the formula Calculate the Simpson community dominance index for each monitoring point in each monitoring month; where S mnis the Simpson community dominance index of the mth monitoring point in the nth monitoring month, is the number of species i at the mth monitoring point in the nth monitoring month, N mn is the number of all species in the community at the mth monitoring point in the nth monitoring month.

[0115] The optimal spatial capture fishing net building blocks include:

[0116] The side length determination submodule is used to determine multiple side length values ​​of the fishing net grid side length according to the grid resolution of the water bird community diversity index grid image;

[0117] The spatial uniformity index acquisition submodule is used to obtain the spatial uniformity index according to the latitude and longitude of the monitoring points arranged in the monitored lake wetland, using the formula Get the spatial uniformity index when the side length of the fishing net grid is equal to each side length value; where C k N is the spatial uniformity index when the side length of the fishing net grid is k; k1 、N kx 、N kn are the number of monitoring points contained in the 1st, xth, and nth fishing net grids containing water bird monitoring points when the side length of the fishing net grid is k; n is the total number of fishing net grids containing monitoring points;

[0118] The optimal spatial capture fishing net construction submodule is used to take the side length corresponding to the minimum value of all spatial uniformity indexes as the most suitable fishing net grid side length, and construct the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index raster image based on the most suitable fishing net grid side length.

[0119] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0120] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A multi-temporal lake wetland waterbird community diversity inversion method, characterized in that: The method comprises: Obtaining water bird community monitoring data at each monitoring point set up in the monitored lake wetland during the monitoring period; the monitoring data includes water bird species and population numbers; Calculate the waterbird community diversity index of each monitoring point in each monitoring month during the monitoring period based on the waterbird community monitoring data; According to the diversity index of water bird communities at all monitoring points in each monitoring month, the nearest neighbor spatial interpolation method was used to perform spatial gridding on the monitored lake wetlands to obtain the grid images of water bird community diversity index of the monitored lake wetlands in each monitoring month. According to the longitude and latitude of the monitoring points arranged in the monitored lake wetland, taking the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization goal, the optimal spatial capture fishing net of the monitored lake wetland is constructed in the water bird community diversity index grid image; Calculate the average value of the water bird community diversity index of all grids within each grid of the optimal spatial capture fishing net, and convert the average value into grid data as the water bird community diversity index of each grid of the monitored lake wetland in each monitoring month, and finally form the water bird community diversity index fishing net image of the monitored lake wetland in each monitoring month during the monitoring period; The method of constructing an optimal spatial capture fishing net for the monitored lake wetland in the water bird community diversity index grid image based on the latitude and longitude of the monitoring points arranged in the monitored lake wetland and taking the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization target specifically includes: Determine multiple values ​​of the side length of the fishing net grid according to the grid resolution of the water bird community diversity index grid image; According to the latitude and longitude of the monitoring points arranged in the monitored lake wetland, the formula Get the spatial uniformity index when the side length of the fishing net grid is equal to each side length value; where C k N is the spatial uniformity index when the side length of the fishing net grid is k; k1 、N kx 、N kn are the number of monitoring points contained in the 1st, xth, and nth fishing net grids containing water bird monitoring points when the side length of the fishing net grid is k; n is the total number of fishing net grids containing monitoring points; The side length value corresponding to the minimum value of all spatial uniformity indexes is used as the most suitable fishing net grid side length, and the optimal spatial capture fishing net of the monitored lake wetland is constructed in the water bird community diversity index grid image based on the most suitable fishing net grid side length.

2. The multi-temporal lake wetland waterbird community diversity inversion method according to claim 1 is characterized in that: The acquisition of water bird community monitoring data at each monitoring point set up in the monitored lake wetland during the monitoring period specifically includes: According to the distribution characteristics of waterbirds in each monitoring month during the monitoring period, the concentrated distribution points of waterbirds are determined as the monitoring points of the monitored lakes and wetlands in each monitoring month; Telescopes are used to monitor and record the water bird community monitoring data at each monitoring point set up in the monitored lake wetlands every monitoring month.

3. The multi-temporal lake wetland waterbird community diversity inversion method according to claim 1 is characterized in that: Based on the water bird community monitoring data, the water bird community diversity index of each monitoring point in each monitoring month during the monitoring period is calculated, including: According to the water bird species and population number of each monitoring point in each monitoring month, the formula The Shannon-Wiener diversity index of each monitoring point in each monitoring month was calculated; mn is the Shannon-Wiener diversity index of the mth monitoring point in the nth monitoring month, is the proportion of individuals of the i-th species in the total number at the m-th monitoring point in the n-th monitoring month; According to the Shannon-Wiener diversity index and the number of waterbird species at each monitoring point in each monitoring month, the formula Calculate the Pielou evenness index of each monitoring point in each monitoring month; where P mn is the Pielou uniformity index of the mth monitoring point in the nth monitoring month, L mn is the total number of species in the community at the mth monitoring point in the nth monitoring month; According to the water bird species and population number of each monitoring point in each monitoring month, the formula Calculate the Simpson community dominance index for each monitoring point in each monitoring month; where S mn is the Simpson community dominance index of the mth monitoring point in the nth monitoring month, is the number of species i at the mth monitoring point in the nth monitoring month, N mn is the number of all species in the community at the mth monitoring point in the nth monitoring month.

4. A multi-temporal lake wetland waterbird community diversity inversion system, characterized in that: The system comprises: A water bird colony monitoring data acquisition module is used to obtain water bird colony monitoring data at each monitoring point arranged in the monitored lake wetland during the monitoring period; the monitoring data includes water bird species and population numbers; A water bird community diversity index calculation module is used to calculate the water bird community diversity index of each monitoring point in each monitoring month during the monitoring period according to the water bird community monitoring data; The water bird community diversity index grid image acquisition module is used to perform spatial grid processing on the monitored lake wetlands based on the water bird community diversity index of all monitoring points in each monitoring month using the nearest neighbor spatial interpolation method to obtain the water bird community diversity index grid image of the monitored lake wetlands in each monitoring month; The optimal spatial capture fishing net construction module is used to construct the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index grid image according to the longitude and latitude of the monitoring points arranged in the monitored lake wetland, taking the maximum value of the uniformity of the distribution of each monitoring point in the fishing net as the optimization target; The water bird community diversity index fishing net image formation module is used to calculate the average value of the water bird community diversity index of all grids within each grid that falls into the optimal spatial capture fishing net, and convert the average value into grid data as the water bird community diversity index of the monitored lake wetland in each grid in each monitoring month, and finally form the water bird community diversity index fishing net image of the monitored lake wetland in each monitoring month during the monitoring period; The optimal space capture fishing net construction module specifically includes: The side length determination submodule is used to determine multiple side length values ​​of the fishing net grid side length according to the grid resolution of the water bird community diversity index grid image; The spatial uniformity index acquisition submodule is used to obtain the spatial uniformity index according to the latitude and longitude of the monitoring points arranged in the monitored lake wetland, using the formula Get the spatial uniformity index when the side length of the fishing net grid is equal to each side length value; where C k N is the spatial uniformity index when the side length of the fishing net grid is k; k1 、N kx 、N kn are the number of monitoring points contained in the 1st, xth, and nth fishing net grids containing water bird monitoring points when the side length of the fishing net grid is k; n is the total number of fishing net grids containing monitoring points; The optimal spatial capture fishing net construction submodule is used to take the side length value corresponding to the minimum value of all spatial uniformity indexes as the most suitable fishing net grid side length, and construct the optimal spatial capture fishing net of the monitored lake wetland in the water bird community diversity index grid image according to the most suitable fishing net grid side length.

5. The multi-temporal lake wetland waterbird community diversity inversion system according to claim 4 is characterized in that: The water bird colony monitoring data acquisition module specifically includes: The monitoring point determination submodule is used to determine the concentrated distribution points of waterbirds as the monitoring points of the monitored lake wetlands in each monitoring month according to the distribution characteristics of waterbirds in each monitoring month during the monitoring period; The water bird colony monitoring data recording submodule is used to use a telescope to monitor and record the water bird colony monitoring data of each monitoring point deployed in the monitored lake wetland every monitoring month.

6. The multi-temporal lake wetland waterbird community diversity inversion system according to claim 4, characterized in that: The water bird community diversity index calculation module specifically includes: The Shannon-Wiener diversity index calculation submodule is used to calculate the number of waterbird species and populations at each monitoring point in each monitoring month using the formula The Shannon-Wiener diversity index of each monitoring point in each monitoring month was calculated; mn is the Shannon-Wiener diversity index of the mth monitoring point in the nth monitoring month, is the proportion of individuals of the i-th species in the total number at the m-th monitoring point in the n-th monitoring month; The Pielou evenness index calculation submodule is used to calculate the number of waterbird species at each monitoring point in each monitoring month according to the Shannon-Wiener diversity index using the formula Calculate the Pielou evenness index of each monitoring point in each monitoring month; where P mn is the Pielou uniformity index of the mth monitoring point in the nth monitoring month, L mn is the total number of species in the community at the mth monitoring point in the nth monitoring month; The Simpson colony dominance index calculation submodule is used to calculate the waterbird species and population size at each monitoring point in each monitoring month using the formula Calculate the Simpson community dominance index for each monitoring point in each monitoring month; where S mn is the Simpson community dominance index of the mth monitoring point in the nth monitoring month, is the number of species i at the mth monitoring point in the nth monitoring month, N mn is the number of all species in the community at the mth monitoring point in the nth monitoring month.

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