CN113486940B - Adaptive bandwidth-based bird waiting migration track reconstruction method - Google Patents
Adaptive bandwidth-based bird waiting migration track reconstruction method Download PDFInfo
- Publication number
- CN113486940B CN113486940B CN202110737780.8A CN202110737780A CN113486940B CN 113486940 B CN113486940 B CN 113486940B CN 202110737780 A CN202110737780 A CN 202110737780A CN 113486940 B CN113486940 B CN 113486940B
- Authority
- CN
- China
- Prior art keywords
- bird
- observation
- waiting
- lon
- lat
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000005012 migration Effects 0.000 title claims abstract description 24
- 238000013508 migration Methods 0.000 title claims abstract description 24
- 230000003044 adaptive effect Effects 0.000 title description 4
- 230000002159 abnormal effect Effects 0.000 claims abstract description 18
- 239000000654 additive Substances 0.000 claims abstract description 5
- 230000000996 additive effect Effects 0.000 claims abstract description 5
- 238000001514 detection method Methods 0.000 claims description 8
- 230000002776 aggregation Effects 0.000 claims description 3
- 238000004220 aggregation Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 3
- 230000007246 mechanism Effects 0.000 abstract description 3
- 238000011160 research Methods 0.000 abstract description 3
- 238000004140 cleaning Methods 0.000 abstract description 2
- 238000010276 construction Methods 0.000 abstract description 2
- 241000271566 Aves Species 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 241000287196 Asthenes Species 0.000 description 1
- 244000025254 Cannabis sativa Species 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 238000013450 outlier detection Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/203—Drawing of straight lines or curves
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a bird waiting migration track reconstruction method based on self-adaptive bandwidth. Some waiting birds have been rendered impractical for individual tracking means due to their minimal size. In order to facilitate the establishment of an effective research protection mechanism for the birds waiting, the invention aims to reconstruct the population track in the bird total migration period by utilizing the bird observing information list according to the eBird citizen data platform. Firstly, carrying out linear interpolation filling on a missing value in bird observing information; then cleaning and removing abnormal data points; on the basis, clustering the bird observing data of each day by using an improved Mean-shift clustering algorithm with self-adaptive bandwidth, and finally, clustering and judging the clustering result of each day by using a traversing algorithm based on minimum cost, and realizing nonlinear fitting by using a generalized additive model. The method has important scientific significance and application value for reconstructing the track of the miniature waiting bird in the migration period, which lacks accurate tracking data, and further provides scientific support for targeted waiting bird protection activities and protection area construction.
Description
Technical Field
The invention relates to a method for reconstructing migration tracks of a bird waiting based on self-adaptive bandwidth, which is used for automatically grouping mass observation data of citizens and reconstructing multiple tracks, and belongs to the field of ecology.
Background
The current rapid changes in global environment and climate pose potential challenges for migrating species that need to cope with or adapt to new conditions. Some waiting birds are extremely sensitive to environmental and climate changes due to extremely small size, high metabolism rate, dependence on nectar, grass seeds and the like as main resources, and individual tracking means such as satellites or GPS (global positioning system) cannot be implemented due to the limitation of the volume and the weight of the waiting birds. In order to conveniently establish an effective research protection mechanism for the birds, track protection in the whole migration period is carried out, mass bird waiting observation information from an eBird citizenship scientific database is used for reconstructing migration tracks in the bird waiting period, and important information such as population change condition, speed change condition, stopover location and the like in the migration period is discovered. The invention aims to reconstruct a population multi-track in a bird migration period according to an eBird citizen data platform created by a Conneler bird laboratory by utilizing the information such as bird name, longitude, latitude, bird observing time and the like in a bird observing information list.
The Mean-Shift clustering algorithm is a density-based unsupervised clustering algorithm, achieves the purpose of clustering by continuously and iteratively searching the area with the highest density, does not need to make the number of clusters in advance, is a widely applied unsupervised clustering algorithm, has fewer used limiting conditions and has extremely strong universality.
Disclosure of Invention
Aiming at the small-sized bird waiting which cannot be accurately tracked, the invention utilizes massive citizen bird observing data to reconstruct multiple tracks in the period of the migration of the bird waiting, thereby helping to mine dynamic information in the migration process and providing effective suggestions for the establishment of a protection area.
The invention aims at realizing the following technical scheme: a method for reconstructing a migration track of a bird based on self-adaptive bandwidth comprises the following steps:
(1) The method comprises the steps of obtaining observation information LAT, LON and D of a certain waiting bird in a year period on a citizen data platform eBird, storing the obtained observation information in an array form, and representing the obtained observation information as follows:
wherein LAT is j For the latitude of the jth observation information of the waiting bird, LON j Longitude, D, of the j-th observation of the waiting bird j Represents the j th day, n is the number of acquired bird waiting observation information, m is the number of days of acquired bird waiting observation information, and m is [1,366 ]],n≥m；
(2) If the repeated observation information data exist, deleting the same observation longitude and latitude information on the same date to reduce the redundancy of the data set, wherein the method is expressed as: when any observation date D is selected j In the time-course of which the first and second contact surfaces,
LAT p ≠LAT q and LON p ≠LON q
Wherein LAT is p ,LAT q For the latitude of any two observation information of the jth weather bird, LON p ,LON q Longitude of any two observation information for the jth weather bird;
(3) The longitude and latitude coordinates LAT and LON of the geographic position observed by the waiting bird are converted into the M-LAT and M-LON of the mercator coordinates, and the M-LON is recorded as:
(4) To ensure the time continuity of the data, the observation date D for the missing in the original data s Its corresponding observation geographic information LAT s ,LON s K nearest neighbor samples (generally, k is 2) are selected, euclidean distance is used as a distance function, and linear interpolation is performed to obtain:
wherein D is t ,D l For D s Observation date of k-nearest neighbor, LAT t ,LAT l Is LAT s Observation site latitude information of k-nearest neighbor, LON t ,LON l Is LON s Observing location longitude information of k-nearest neighbor, and l < s < t;
combining the obtained interpolation into an uninterpolated data set to obtain:
wherein N is the number of the obtained bird waiting observation information after interpolation, M is the number of days of the obtained bird waiting observation information after interpolation, and m=365 or m=366; LM-LAT, LM-LON, LD is the observation information longitude and latitude obtained after interpolation and observation date;
(5) In order to remove abnormal data points in citizen observation data sets, aiming at the characteristics of a spatial data set, an abnormal point detection algorithm based on spatial data point influence factors is adopted:
LM-LAT, LM-LON is defined as dataset X= { X 1 ,X 2 ,...X N Weight set
The spatial k-distance neighborhood N (k, O) of the spatial object O can be expressed as:
where k is the distance of a given spatial object o, dist (p, o, ω) is the distance of the spatial objects p and o;
space object X i And X is j The distance of (2) may be defined as:
wherein x is ik ,x jk Respectively X i ,X j F is a normalization function;
the average μ of an object to all neighbors within its neighborhood can be noted as:
wherein N is K (p) represents the spatial k-distance neighborhood of object p, |N K (p) | is the number of all neighbors in the neighborhood;
spatial offset rate of object pThe method comprises the following steps:
spatial offset influence rate of object pThe method comprises the following steps:
the spatial offset influence factor of the object p can be obtainedThe method comprises the following steps:
removing abnormal points with larger offset influence factors in the observed data points to improve the data quality, and obtaining the following steps:
wherein r is the number of the bird waiting observation information obtained after abnormal points are removed, and DLM-LAT and DLM-LON are the longitude and latitude of the observation information obtained through the processing of an abnormal point detection algorithm;
(6) Because the daily observation data amount of a certain kind of waiting birds in the observation data set is large in difference, the sliding window idea is adopted to aggregate the observation data of seven continuous days into the observation data of the first day in seven days to increase the stability of clustering, and the method comprises the following steps of:
the HDLM-LAT and the HDLM-LON are longitude and latitude of observation information obtained through sliding window idea aggregation;
(7) Clustering and grouping the daily bird waiting observation data by adopting a Mean-Shift algorithm based on self-adaptive bandwidth, generating the size of a Mean-Shift window by utilizing a bandwidth estimation algorithm, wherein the size is expressed as that P samples are randomly selected from the bird waiting observation data to be classified, and P is the point of any sample i The distance between each pair of samples in the unsupervised n-nearest neighbor (the ratio of nearest neighbor to sample in nearest neighbor search is recommended to be 0.3-0.7) search result is defined as follows:
M＝(m 1 ,m 2 ,...,m n )
selecting the maximum distance in M
Calculating the sum of the farthest pairing distances of the unsupervised n-nearest neighbor searches of the P samples, and marking as:then selectAs the final bandwidth, using a Mean-Shift algorithm to obtain an unsupervised observation clustering result of each day;
(8) According to the unsupervised observation clustering condition of each day, performing distance traversal on the clustering centers of two adjacent days by using a traversal algorithm based on the minimum cost:
calculating the distance D between any two clustering centers in two adjacent days by applying a tree-structured layer sequence traversal method:
wherein, (x) 1 ,x 2 ),(y 1 ,y 2 ) D is the coordinate of the clustering center under the ink card support coordinate system, and is proportional to the cost;
obtaining grouping conditions of population clustering centers in an annual period;
wherein, T represents the total number of groups of waiting birds, which is equal to the number of the finally fitted tracks;
(9) Utilizing a Generalized Additive Model (GAM) to respectively perform nonlinear fitting on longitude and latitude of the bird waiting observation grouping data in the annual period and time, thereby obtaining the functional relation between longitude and latitude longitude and time t respectively:
latitude＝g(t)；longitude＝h(t)
(10) And finding out corresponding longitude and latitude coordinates on the same date, and connecting geographic positions of all dates to obtain a final reconstruction result of the migration track of the bird.
The beneficial effects of the invention are as follows: in order to facilitate the establishment of effective research protection mechanisms for the waiting birds, the invention aims to reconstruct population trajectories in bird total migration periods by utilizing an eBird citizen data platform and utilizing an sightseeing information list thereof. Firstly, linear interpolation filling is carried out on the missing value in the bird observing information, and the time continuity of bird observing data is maintained; then cleaning and removing abnormal data points by adopting an abnormal point detection algorithm based on a space data point influence factor, so as to improve the data quality; on the basis, the improved self-adaptive bandwidth unsupervised clustering algorithm Mean-shift is utilized to carry out density clustering on the bird observing data of each day, and finally, the clustering result of each day is subjected to grouping judgment by utilizing the traversing algorithm based on the minimum cost, and nonlinear fitting is realized by utilizing the generalized additive model, so that the reconstructed multi-track result is obtained. The method has important scientific significance and application value for reconstructing the track of the miniature waiting bird in the migration period, which lacks accurate tracking data, and further provides scientific support for targeted waiting bird protection activities and protection area construction.
Drawings
FIG. 1 is a flow chart of a method for reconstructing a bird migration trajectory based on adaptive bandwidth according to the present invention;
FIG. 2 is a graph showing the comparison of the results before and after the detection of the abnormal point in the embodiment of the present invention;
FIG. 3 is a schematic diagram of layer sequence traversal grouping according to an embodiment of the invention;
fig. 4 (a) and (b) are schematic diagrams of longitude and latitude as a function of time according to embodiments of the present invention;
FIG. 5 is a schematic illustration of 4 bird migration trajectories fitted in an embodiment of the present invention;
FIG. 6 is a graph comparing the distribution of reconstructed trajectories with raw observation data in an embodiment of the invention.
Detailed Description
The invention is further described below with reference to the drawings and specific embodiments. The following examples are only for more clearly illustrating the technical solution of the present invention and are not intended to limit the scope of the present invention.
As shown in fig. 1, the method for reconstructing a migration track of a bird based on adaptive bandwidth provided by the embodiment of the invention comprises the following steps:
(1) Taking a small-ringing bird (Anthus_sprapeii) as an example in North America in this example, the 2018-year observation information arrays LAT, LON, D on the citizen data platform eBird are obtained, and are expressed as:
wherein LAT is j For the latitude of the jth observation information of the waiting bird, LON j Longitude, D, of the j-th observation of the waiting bird j Day j is indicated.
(2) Checking and deleting on the same dayThe same observed longitude and latitude information under the period to reduce data set redundancy is expressed as: when any observation date D is selected j In the time-course of which the first and second contact surfaces,
LAT p ≠LAT q and LON p ≠LON q
Wherein LAT is p ,LAT q For the latitude of any two observation information of the jth weather bird, LON p ,LON q Longitude of any two observations for the jth bird.
(3) The longitude and latitude coordinates LAT and LON of the geographic position observed by the waiting bird are converted into the M-LAT and M-LON of the mercator coordinates, and the M-LON is recorded as:
(4) To ensure the time continuity of the data, the observation date D for the missing in the original data s Its corresponding observation geographic information LAT s ,LON s K nearest neighbor samples (generally, k is 2) are selected, euclidean distance is used as a distance function, and linear interpolation is performed to obtain:
wherein D is t ,D l For D s Observation date of k-nearest neighbor, LAT t ,LAT l Is LAT s Observation site latitude information of k-nearest neighbor, LON t ,LON l Is LON s Observing location longitude information of k-nearest neighbor, and l < s < t;
combining the obtained interpolation into an uninterpolated data set to obtain:
wherein, LM-LAT, LM-LON, LD is the observation information longitude and latitude and observation date obtained after interpolation.
(5) In order to remove abnormal data points in citizen observation data sets, aiming at the characteristics of a spatial data set, an abnormal point detection algorithm based on spatial data point influence factors is adopted:
LM-LAT, LM-LON is defined as dataset X= { X 1 ,X 2 ,...X N Weight set
The spatial k-distance neighborhood N (k, O) of the spatial object O can be expressed as:
where k is the distance of a given spatial object o, dist (p, o, ω) is the distance of the spatial objects p and o;
space object X i And X is j The distance of (2) may be defined as:
wherein x is ik ,x jk Respectively X i ,X j F is a normalization function;
the average μ of an object to all neighbors within its neighborhood can be noted as:
wherein N is K (p) represents the spatial k-distance neighborhood of object p, |N K (p) isThe number of all neighbors in the neighborhood;
spatial offset rate of object pThe method comprises the following steps:
spatial offset influence rate of object pThe method comprises the following steps:
the spatial offset influence factor of the object p can be obtainedThe method comprises the following steps:
removing abnormal points with larger offset influence factors in the observed data points to improve the data quality, and obtaining the following steps:
the DLM-LAT and the DLM-LON are longitude and latitude of observation information obtained through processing of an outlier detection algorithm;
fig. 2 is a graph showing the comparison of the results before and after the detection of the abnormal point in the present embodiment.
(6) Because the daily observation data amount of a certain kind of waiting birds in the observation data set is large in difference, the sliding window idea is adopted to aggregate the observation data of seven continuous days into the observation data of the first day in seven days to increase the stability of clustering, and the method comprises the following steps of:
the HDLM-LAT and the HDLM-LON are longitude and latitude of observation information obtained through sliding window idea aggregation.
(7) Clustering and grouping the daily bird waiting observation data by adopting a Mean-Shift algorithm based on the self-adaptive bandwidth, generating the size of a Mean-Shift window by utilizing a bandwidth estimation algorithm, wherein the size is expressed as that P samples are randomly selected from the bird waiting observation data to be classified, in the embodiment, P=500 is taken, and for any sample point P i The distance between each pair of samples in the unsupervised n-nearest neighbor (the ratio of nearest neighbor to sample in the nearest neighbor search is 0.5 in this embodiment) search result is defined as follows:
M＝(m 1 ,m 2 ,...,m n )
selecting the maximum distance in M
Calculating the sum of the farthest pairing distances of 500 sample unsupervised n-nearest neighbor searches, and recording asThen selectAs the final bandwidth, the Mean-Shift algorithm was used to obtain daily unsupervised observation clusters.
(8) According to the unsupervised observation clustering condition of each day, performing distance traversal on the clustering centers of two adjacent days by using a traversal algorithm based on the minimum cost:
calculating the distance D between any two clustering centers in two adjacent days by applying a tree-structured layer sequence traversal method:
wherein, (x) 1 ,x 2 ),(y 1 ,y 2 ) D is the coordinate of the clustering center under the ink card support coordinate system, and is proportional to the cost;
obtaining grouping conditions of population clustering centers in an annual period:
in this embodiment, the number of groups of waiting birds is 4, which is equal to the number of the finally fitted tracks;
fig. 3 is a schematic diagram of layer sequence traversal in this embodiment, where two layers of candidate bird icons respectively represent Mean-Shift unsupervised clustering results of two adjacent days, candidate bird cluster centers with reference numbers 1,2, and 3 respectively perform distance traversal calculation with reference numbers 4 and 5, and L (1, 4) is the minimum distance after the traversal calculation for the candidate bird cluster center with reference number 1, so that two populations represented by the candidate bird cluster centers with reference numbers 1 and 4 can be considered to be the same population, and so on, to complete grouping calculation.
(9) Utilizing a Generalized Additive Model (GAM) to respectively perform nonlinear fitting on longitude and latitude of the bird waiting observation grouping data in the annual period and time, thereby obtaining the functional relation between longitude and latitude longitude and time t respectively:
latitude＝g(t)；longitude＝h(t)
fig. 4 (a) and (b) are schematic diagrams of longitude and latitude as a function of time in the embodiment of the present invention.
(10) And finding out corresponding longitude and latitude coordinates on the same date, and connecting geographic positions of all dates to obtain a final reconstruction result of the migration track of the bird.
FIG. 5 is a schematic illustration of 4 bird migration trajectories fitted in an embodiment of the present invention; FIG. 6 is a schematic diagram comparing the reconstructed trajectory with the original observed data distribution in an embodiment of the present invention; therefore, the method can obtain a better reconstruction effect of the migration track of the bird.
The foregoing is merely a preferred embodiment of the present invention, and the present invention has been disclosed in the above description of the preferred embodiment, but is not limited thereto. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention or modifications to equivalent embodiments using the methods and technical contents disclosed above, without departing from the scope of the technical solution of the present invention. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present invention still fall within the scope of the technical solution of the present invention.
Claims (3)
1. A method for reconstructing a migration track of a bird based on self-adaptive bandwidth is characterized by comprising the following steps:
(1) The method comprises the steps of obtaining observation information LAT, LON and D of a certain waiting bird in a year period on a citizen data platform eBird, storing the obtained observation information in an array form, and representing the obtained observation information as follows:
wherein LAT is j For the latitude of the jth observation information of the waiting bird, LON j Longitude, D, of the j-th observation of the waiting bird j Represents the j th day, n is the number of acquired bird waiting observation information, m is the number of days of acquired bird waiting observation information, and m is [1,366 ]],n≥m；
(2) If the repeated observation information data exist, deleting the same observation longitude and latitude information on the same date so as to reduce the redundancy of the data set;
(3) Converting longitude and latitude coordinates LAT and LON of a geographic position observed by a waiting bird into mercator coordinates, and marking the mercator coordinates as M-LAT and M-LON;
(4) To ensure the time continuity of the data, the observation date D for the missing in the original data s Its corresponding observation geographic information LAT s ,LON s Selecting k nearest neighbor samples, taking Euclidean distance as a distance function, performing linear interpolation, marking the longitude and latitude and the observation date of the observed information obtained by interpolation as LM-LAT, LM-LON and LD, marking the number of the bird waiting observed information as N and marking the number of days of the bird waiting observed information as M;
(5) In order to remove abnormal data points in citizen observation data sets, aiming at the characteristics of a spatial data set, an abnormal point detection algorithm based on spatial data point influence factors is adopted:
LM-LAT, LM-LON is defined as dataset X= { X 1 ,X 2 ,...X N Weight set
The spatial k-distance neighborhood N (k, O) of the spatial object O can be expressed as:
where k is the distance of a given spatial object o, dist (p, o, ω) is the distance of the spatial objects p and o;
space object X i And X is j The distance of (2) may be defined as:
wherein x is ik ,x jk Respectively X i ,X j F is a normalization function;
the average μ of an object to all neighbors within its neighborhood can be noted as:
wherein N is K (p) represents a pair ofSpace k like p is from the neighborhood, |N K (p) | is the number of all neighbors in the neighborhood;
spatial offset rate of object pThe method comprises the following steps:
spatial offset influence rate of object pThe method comprises the following steps:
the spatial offset influence factor of the object p can be obtainedThe method comprises the following steps:
removing abnormal points with larger offset influence factors in the observed data points to improve the data quality, and obtaining the following steps:
wherein r is the number of the bird waiting observation information obtained after abnormal points are removed, and DLM-LAT and DLM-LON are the longitude and latitude of the observation information obtained through the processing of an abnormal point detection algorithm;
(6) The sliding window concept is adopted to aggregate the observation data of seven continuous days into the observation data of the first day in seven days to increase the clustering stability, and the longitude and latitude of the observation information obtained through the sliding window concept aggregation are recorded as HDLM-LAT and HDLM-LON;
(7) Clustering and grouping the daily bird waiting observation data by adopting a Mean-Shift algorithm based on self-adaptive bandwidth, generating the size of a Mean-Shift window by utilizing a bandwidth estimation algorithm, wherein the size is expressed as that P samples are randomly selected from the bird waiting observation data to be classified, and P is the point of any sample i The distance of each pair of samples in the unsupervised n-nearest neighbor search result is defined as follows:
M＝(m 1 ,m 2 ,...,m n )
selecting the maximum distance in M
Calculating the sum of the farthest pairing distances of the unsupervised n-nearest neighbor searches of the P samples, and marking as:then select +.>As the final bandwidth, using a Mean-Shift algorithm to obtain an unsupervised observation clustering result of each day;
(8) According to the unsupervised observation clustering condition of each day, performing distance traversal on the clustering centers of two adjacent days by using a traversal algorithm based on the minimum cost:
calculating the distance D between any two clustering centers in two adjacent days by applying a tree-structured layer sequence traversal method:
wherein, (x) 1 ,x 2 ),(y 1 ,y 2 ) D is the coordinate of the clustering center under the ink card support coordinate system, and is proportional to the cost;
obtaining grouping conditions of population clustering centers in an annual period;
wherein, T represents the total number of groups of waiting birds, which is equal to the number of the finally fitted tracks;
(9) Respectively performing nonlinear fitting on longitude and latitude of the bird waiting observation grouping data in the annual period and time by using a Generalized Additive Model (GAM), so as to obtain the functional relation between the longitude and the latitude and the time;
(10) And finding out corresponding longitude and latitude coordinates on the same date, and connecting geographic positions of all dates to obtain a final reconstruction result of the migration track of the bird.
2. The method according to claim 1, wherein deleting the same observed latitude and longitude information on the same date in the step (2) is specifically expressed as: when any observation date D is selected j In the time-course of which the first and second contact surfaces,
LAT p ≠LAT q and LON p ≠LON q
Wherein LAT is p ,LAT q For the latitude of any two observation information of the jth weather bird, LON p ,LON q Longitude of any two observations for the jth bird.
3. The method of claim 1, wherein in step (4), k is 2, and the formula of the linear interpolation is as follows:
wherein D is t ,D l For D s Observation date of k-nearest neighbor, LAT t ,LAT l Is LAT s Observation site latitude information of k-nearest neighbor, LON t ,LON l Is LON s Observing location longitude information of k-nearest neighbor, and l < s < t;
combining the obtained interpolation into an uninterpolated data set to obtain:
where m=365 or m=366.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110737780.8A CN113486940B (en) | 2021-06-30 | 2021-06-30 | Adaptive bandwidth-based bird waiting migration track reconstruction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110737780.8A CN113486940B (en) | 2021-06-30 | 2021-06-30 | Adaptive bandwidth-based bird waiting migration track reconstruction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113486940A CN113486940A (en) | 2021-10-08 |
| CN113486940B true CN113486940B (en) | 2023-10-31 |
Family
ID=77936845
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110737780.8A Active CN113486940B (en) | 2021-06-30 | 2021-06-30 | Adaptive bandwidth-based bird waiting migration track reconstruction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113486940B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014180241A (en) * | 2013-03-19 | 2014-09-29 | Tasada Kosakusho:Kk | Migratory bird observation system and transmitter for the same |
| CN106940718A (en) * | 2017-03-14 | 2017-07-11 | 山东师范大学 | A kind of method, device and server for obtaining migratory bird moving path description |
| CN108268053A (en) * | 2018-01-11 | 2018-07-10 | 北京航空航天大学 | The autonomous cluster formation rotation control method of unmanned plane of imitative migratory bird evolution Snowdrift game |
| CN112396626A (en) * | 2020-12-24 | 2021-02-23 | 西安费斯达自动化工程有限公司 | Sequence image population identification method for monitoring migratory bird migration |
-
2021
- 2021-06-30 CN CN202110737780.8A patent/CN113486940B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014180241A (en) * | 2013-03-19 | 2014-09-29 | Tasada Kosakusho:Kk | Migratory bird observation system and transmitter for the same |
| CN106940718A (en) * | 2017-03-14 | 2017-07-11 | 山东师范大学 | A kind of method, device and server for obtaining migratory bird moving path description |
| CN108268053A (en) * | 2018-01-11 | 2018-07-10 | 北京航空航天大学 | The autonomous cluster formation rotation control method of unmanned plane of imitative migratory bird evolution Snowdrift game |
| CN112396626A (en) * | 2020-12-24 | 2021-02-23 | 西安费斯达自动化工程有限公司 | Sequence image population identification method for monitoring migratory bird migration |
Non-Patent Citations (2)
| Title |
|---|
| 从自然保护区到国家公园体制试点:三江源国家公园环境覆盖的变化及其对两栖爬行类保护的启示;乔慧捷;汪晓意;王伟;罗振华;唐科;黄燕;杨胜男;曹伟伟;赵新全;江建平;胡军华;;生物多样性(第02期);全文 * |
| 湖南屏风界候鸟种类与迁徙规律调查;胡中常;袁志湘;刘继群;覃晓春;廖汉军;野生动物;第28卷(第1期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113486940A (en) | 2021-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sun et al. | An overview of moving object trajectory compression algorithms | |
| CN112182410B (en) | User travel mode mining method based on space-time track knowledge graph | |
| Mitchell et al. | Reconstructing the reproductive mode of an Ediacaran macro-organism | |
| Thompson et al. | Monitoring vertebrate populations | |
| Buckland et al. | Abundance estimation | |
| Lee et al. | Precision, accuracy, and costs of survey methods for giraffe Giraffa camelopardalis | |
| CN107832778A (en) | A kind of same target recognition methods based on spatial synthesis similarity | |
| CN111916144A (en) | Protein classification method based on self-attention neural network and coarsening algorithm | |
| CN112598165A (en) | Private car data-based urban functional area transfer flow prediction method and device | |
| Gupta et al. | Extended biogeography based optimization for natural terrain feature classification from satellite remote sensing images | |
| Khanh Ni et al. | Mapping submerged aquatic vegetation along the central Vietnamese coast using multi-source remote sensing | |
| Wong et al. | Acoustic assessment of polymetallic nodule abundance using sidescan sonar and altimeter | |
| Maglietta et al. | Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea | |
| CN113486940B (en) | Adaptive bandwidth-based bird waiting migration track reconstruction method | |
| Charbonneau et al. | Microscale and mesoscale aeolian processes of sandy coastal foredunes from background to extreme conditions | |
| Loraamm et al. | A time-geographic approach to identifying daily habitat use patterns for Amazonian Black Skimmers | |
| Maleki et al. | Wetland restoration prioritization using artificial neural networks | |
| Roy et al. | Deep inference of seabird dives from GPS-only records: Performance and generalization properties | |
| Kumar et al. | Data clustering using differential search algorithm | |
| Christina et al. | Analysis of hard clustering algorithms applicable to regionalization | |
| CN109508749A (en) | A kind of cluster analysis system and method based on deep knowledge expression | |
| Lucas et al. | Exploring data quantity requirements for domain adaptation in the classification of satellite image time series | |
| Wang et al. | Grid‐Based Whole Trajectory Clustering in Road Networks Environment | |
| Tsukahara et al. | Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer | |
| Trahms et al. | Data fusion for connectivity analysis between ocean regions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |