JP6338101B2 - Flight possibility score calculation system - Google Patents

Description

  The present invention relates to a flying possibility score calculation method capable of quantitatively predicting the species composition of a plurality of living creatures (birds, butterflies, dragonflies, etc.), and a flying possibility score using the method. It relates to a calculation system.

  2. Description of the Related Art Conventionally, a measurement method for remote sensing a state on the ground surface using an image pickup device mounted on an artificial satellite or an aircraft, or data acquired by a radar has been performed. Such remote sensing technology has begun to be actively used in the fields of agriculture, water resources, or city planning.

For example, Non-Patent Document 1 discloses the height of trees that can be grasped from the difference between the green cover data obtained from satellite data with high resolution and the ground surface model (DSM) and topographic model (DTM) using remote sensing technology. Overlapping data to interpret the status of green areas in the evaluation area, select Kogera as an environmental indicator species indicating the ecology of the urban area, and use the Geographic Information System (GIS) The technology that examines the alternatives of the project, such as the development of new roads and green spaces, by visual evaluation of the impact of the development project is disclosed.
“Development of Ecological Network Evaluation Technology” (Kashima Construction Co., Ltd.), http: // www. kajima. co. jp / news / press / 200809 / 17c1-j. htm

  In Non-Patent Document 1, a simulation is performed according to conditions such as how high, kind, and scale of green space in the development area can expand the habitat and activity range of Kogera. It is disclosed that it is possible.

  However, with respect to a technique for quantitatively predicting what kind of organisms may fly in urban green spaces, as described in Non-Patent Document 1, conventionally, the technique is mainly limited to a single species as an index. Then, the method of predicting based on the habitat conditions was taken.

  On the other hand, there has been a problem that a technique for quantitatively predicting the species composition of a plurality of living creatures (such as birds, butterflies and dragonflies) has not been established. For some, methods for predicting species composition based on creature information and site environmental conditions have also been studied, but qualitative prediction is the focus, and it is difficult to predict the possibility of flying quantitatively. there were.

  In addition, it is difficult to predict biota with high possibility of flying, especially reflecting the quality and distribution of green spaces that differ from region to region, and the different habitat conditions (such as the distance traveled and the environment selected by flying organisms). Met.

  In short, the calculation of the flying possibility of flying creatures has been developed even if prediction by human hands based on actual survey information is required or prediction by human hands is unnecessary. There was a problem that small-scale green areas such as the land were excluded, and changes in the flying biota before and after the greening project at the development site could not be predicted.

This invention solves the said subject, and the invention which concerns on Claim 1 is the data about the area for every green space unit which is a land block covered with green space in the object area | region, and the ratio of a vegetation type. and green spaces database which turned into, for each species of flying organisms, and green the area score according to the area scale, and vegetation type score according to the vegetation, and the home range, and habitat conditions database into data, and data on the green database a selection unit that selects the green units being of the per greenery unit selected by the selection processing section, based on the habitat condition database, available to calculate the availability score for each species flying organism and sex score calculation process unit, the per greenery unit selected by the selecting section, on the basis of the green database and the living condition database, for each type of flying organisms A selectable score calculation processing unit for calculating a selectable score, the availability score calculation process and the availability score calculated by the unit, the selectability selectable score calculated by the score calculation process section from a flying potential score calculation system comprising: the flying potential score calculation processing unit for calculating a flying potential scores for each type of flying organisms, the.

The invention according to claim 2 is the flight possibility score calculation system according to claim 1, wherein the green space database is configured based on remote sensing data.

The invention according to claim 3 is the flight possibility score calculation system according to claim 2, further characterized in that the green space database is configured based on virtual green space data.

  According to the flight possibility score calculation method and the flight possibility score calculation system using the method according to the present invention, the movement and habitat environment of each flying species are reflected, in particular, with the quality and distribution of green spaces that differ from region to region. In addition, it is possible to quantitatively predict the biota that has a high possibility of flying, and it is not necessary to perform prediction by human hands based on actual survey information.

  In addition, according to the flying possibility score calculation method and the flying possibility score calculation system using the method according to the present invention, as a greening effect in the development site, a flying biota having a high possibility of flying before and after the project. It became possible to compare quantitatively.

It is a figure which shows an example of the system configuration which performs the flying possibility score calculation method which concerns on embodiment of this invention. It is a partially expanded view of remote sensing data used in the flying possibility score calculation method according to the embodiment of the present invention. It is a figure which shows the flowchart of the process for producing the green space database used with the flying possibility score calculation method which concerns on embodiment of this invention from remote sensing data or GIS data. It is a figure which shows an example of the data structure of the green space database used with the flying possibility score calculation method which concerns on embodiment of this invention. It is a figure which shows an example of the data structure of the habitat condition database used with the flying possibility score calculation method which concerns on embodiment of this invention. It is a figure which shows the flowchart of the flying possibility score calculation process by the flying possibility score calculation method system which concerns on embodiment of this invention. Availability score is a diagram showing a flowchart of a subroutine of (US _n) calculation processing. It shows a flowchart of a subroutine of selectable score (SS _n) calculation processing. Flying potential score is a diagram showing a flowchart of a subroutine of (IS _n) calculation processing. It is a partially enlarged view of remote sensing data used in a flying possibility score calculation method according to another embodiment of the present invention. It is a figure which shows an example of the data structure of the green space database used with the flying possibility score calculation method which concerns on other embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing an example of a system configuration for executing a flying possibility score calculation method according to an embodiment of the present invention. In FIG. 1, 10 is a system bus, 11 is a CPU (Central Processing Unit), 12 is a RAM (Random Access Memory), 13 is a ROM (Read Only Memory), 14 is a communication control unit that controls communication with an external information device, 15 is an input control unit such as a keyboard controller, 16 is an output control unit such as a display controller, 17 is an external storage device control unit, 18 is an input unit including input devices such as a keyboard, pointing device, and mouse, 19 is an LCD display, etc. An output unit 20 including a display device and a printing device 20 is an external storage device such as an HDD (Hard Disk Drive).

  In FIG. 1, the CPU 11 retrieves and acquires data by communicating with an external device in accordance with a program ROM stored in the ROM 13 or a program stored in the large-capacity external storage device 20. Processing of output data in which graphics, images, characters, tables, etc. are mixed is executed, and management of a database stored in the external storage device 20 is further executed.

  Further, the CPU 11 comprehensively controls each device connected to the system bus 10. The program ROM in the ROM 13 or the external storage device 20 stores an operating system program (hereinafter referred to as OS) that is a basic program for controlling the CPU 11. The ROM 13 or the external storage device 20 stores various data used when performing output data processing or the like. The RAM 12 functions as a main memory and work area for the CPU 11.

  The input control unit 15 controls the input unit 18 from a keyboard or a pointing device (not shown). The output control unit 16 controls output of a display device such as an LCD display and a printing device such as a printer.

The external storage device control unit 17 stores a boot program, various applications, font data, user files, edit files, printer drivers, and the like.
Disk Drive), or in some cases, controls access to the external storage device 20 such as a flexible disk (FD). The communication control unit 14 controls communication with an external device via a network. As a result, the data required by the system can be acquired from a database held by an external device on the Internet or an intranet, or information can be transmitted to the external device.

  In addition to an operating system program (hereinafter referred to as OS) that is a control program for the CPU 11, the external storage device 20 includes a system program for operating the flying possibility score calculation method of the present invention on the CPU 11, data used in the system program, and the like. Installed, saved and remembered.

  The data used in the system program for realizing the flight possibility score calculation method of the present invention is image data (remote sensing data) obtained by imaging an evaluation target area from a flying object such as an artificial satellite or an airplane. Although it is assumed that the data is stored in the external storage device 20, in some cases, the data can be obtained from an external device on the Internet or an intranet via the communication control unit 14. is there.

  FIG. 2 is a partially enlarged view of remote sensing data used in the flying possibility score calculation method according to the embodiment of the present invention. In the present embodiment, a multi-spectral image is used as such remote sensing data, and the land cover situation on the ground is extracted. In the example of FIG. 2, one mesh has a size of 2 m × 2 m. The example of FIG. 2 shows that there are three green space units that are land sections covered with green space.

  Here, the process for preparing the green space database used with the flying possibility score calculation method which concerns on such embodiment of this invention is demonstrated. FIG. 3 is a diagram showing a flowchart of processing for creating a green space database used in the flying possibility score calculation method according to the embodiment of the present invention from image data (remote sensing data).

  In FIG. 3, when the preparation process of the flying possibility score calculation method according to the present embodiment is started in step S100, the process proceeds to step S101, and the satellite image data (remote sensing data) in the target region is changed to FIG. The green space unit extraction process as shown is executed. In this step, reference is made to vegetation maps and GIS data relating to vegetation by public institutions. The target area can be set to an arbitrary area, but can be set, for example, within a radius of several kilometers from the target business site for which a greening plan or the like is being considered.

  Next, in step S102, the ratio of deciduous trees to the entire green area unit in each green area unit is calculated. In step S103, the ratio of evergreen trees and conifers to the entire green area unit in each green area unit is calculated. In S104, the ratio of the high stem herb to the whole green area unit in each green area unit is calculated, and in Step S105, the ratio of the low stem herb to the whole green area unit is calculated in step S105.

  Through the above processes, a map as shown in FIG. 2 and a green space database can be obtained from remote sensing data and GIS data. FIG. 4 is a diagram showing an example of the data structure of the green space database used in the flying possibility score calculation method according to the embodiment of the present invention.

  FIG. 4 shows an example in which N green space units exist in the map. In the green space database, a unique ID is assigned to each green space unit. The green space database stores mesh position data on a map as shown in FIG. 2 for each green space. Data relating to “area” is stored in the green space database. Naturally, the data relating to the “area” is proportional to the number of meshes constituting the green space unit.

In the green space database, “the ratio of deciduous trees”, “the ratio of evergreen trees and conifers”, “the ratio of high stem herbs”, and “the ratio of low stem herbs” are stored. The total of “deciduous tree ratio”, “evergreen / conifer ratio”, “high stem herb ratio”, and “low stem herb ratio” in each green area unit is 1.

Next, in the flight possibility score calculation method according to the present invention, a habitat condition database is prepared. FIG. 5 is a diagram showing an example of the data structure of the habitat condition database used in the flying possibility score calculation method according to the embodiment of the present invention. The habitat condition database is a data collection of the green cover area score related to area size, the vegetation type score related to vegetation, and the home range for each species of flying organisms such as birds, butterflies and dragonflies. is there. In the habitat condition database, SP ₁ , SP ₂ , SP ₃ ,... SP _X indicate species of flying organisms, respectively.

  In addition, in the habitat condition database, the green cover area score and the vegetation type score for each species are calculated by referring to the habitat environment data of flying creatures (birds, butterflies, dragonflies, etc.) based on past literature information. Stored.

  The green cover area score is based on the classification of green space units by area scale (less than 0.25 ha, 0.25 ha to less than 1.0 ha, 1.0 ha to less than 5.0 ha, 5.0 ha to less than 10 ha, 10 ha or more). For each classification, the habitability (scored in 4 stages from 1 to 4) is scored.

For example, looking at the SP ₁ species, the score of p ₁ is less than 0.25 ha, the score of q ₁ is less than 0.25 ha and less than 1.0 ha, and the score of r ₁ is 1.0 to less than 5.0 ha. However, a score of s ₁ is given at 5.0 ha or more and less than 10 ha, and a score of t ₁ is given at 10 ha or more. Each of the scores p ₁ , q ₁ , r ₁ , s ₁ , and t ₁ takes any one of values 1, 2, 3, and 4.

  The vegetation type score is classified as “deciduous tree”, “evergreen tree / coniferous tree”, “high stem herb”, “low stem herb”, and the habitability (4 points from 1 to 4) for each class. ) Is scored.

For example, if we look for the species of SP _1, the score of the deciduous trees a ₁ is, the score of b ₁ in the evergreen, conifers, score of c ₁ in the high stalk herb is given a score of d ₁ is at a low stems herbaceous ing. Each of the scores a ₁ , b ₁ , c ₁ , and d ₁ takes one of values 1, 2, 3, and 4.

  In the home range, data related to the home zone for each species of flying creatures is stored.

  Next, a method for calculating the flying possibility score based on the database as described above will be described.

  FIG. 6 is a view showing a flowchart of the flying possibility score calculation process by the flying possibility score calculation method system according to the embodiment of the present invention.

  In step 201, first, a green space unit to be calculated is selected. Here, a case where a green space unit with a green space ID of 1 is selected is taken as an example, and a flying possibility score for each species of flying organisms flying to a green space unit with a green space ID of 1 is calculated. Other green space units can be handled similarly.

In the next step S202, a subroutine of the availability score (US _n ) calculation process is executed. FIG. 7 is a flowchart of a subroutine of the usability score (US _n ) calculation process.

Step S300 When the subroutine of the availability score (US _n ) calculation process is started, subsequently, in step S301, data related to (green space ID) = 1 selected in the previous selection step is acquired from the green space database. . Moreover, each parameter of the habitat condition database is acquired.

  Next, in step S302, 1 is set to the variable n.

In step S303, it is determined whether or not S ₁ is less than 0.25ha. If the determination result is YES, the process proceeds to the next step S304, and the availability score (US _n ) is set to p _n × Ln (S ₁₎ calculated by _{× (a n A 1 + b} n B 1 + c n C 1 + d n D 1), and stores. Where Ln
Is the natural logarithm. In addition, since S ₁ is less than 0.25 ha, _pn is used as the green coverage area score in the habitat condition database in the calculation formula. (See FIG. 5. The concept is the same hereinafter.)
On the other hand, if the determination result of step S303 is NO, the process proceeds to step S305.

In step S305, whether or not S ₁ is less than or 0.25ha 1.0ha is determined, and if the decision result is YES, the process proceeds to the next step S306, the availability score (US _n) q _n × calculated by _{Ln (S 1) × (a} n A 1 + b n B 1 + c n C 1 + d n D 1), and stores. On the other hand, if the determination result of step S305 is NO, the process proceeds to step S307.

At step S307, S ₁ it is determined whether it is less than 1.0ha 5.0ha, the decision result is YES, the process proceeds to the next step S308, the availability score (US _n
) Was calculated by _{r n × Ln (S 1)} × (a n A 1 + b n B 1 + c n C 1 + d n D 1), and stores. On the other hand, if the determination result of step S307 is NO, the process proceeds to step S309.

At step S309, S ₁ it is determined whether it is less than 5.0ha 10ha,
If the determination result is YES, the process proceeds to the next step S310, and the availability score (US _n )
The s _n × Ln (S ₁₎ is calculated by _{× (a n A 1 + b} n B 1 + c n C 1 + d n D 1), and stores. On the other hand, if the determination result of step S309 is NO, the process proceeds to step S311.

In step S311, whether or not S ₁ is at least 10ha is determined, the determination result YE
If it is S, the process proceeds to the next step S312, the calculation by the availability score (US _n) to _{t n × Ln (S 1)} × (a n A 1 + b n B 1 + c n C 1 + d n D 1) And remember. On the other hand, step S
If the determination result in 311 is NO, the process proceeds to step S313.

In step S313, it is determined whether n = X. If the determination in step S313 is no, the process proceeds to step S314, where n is incremented by 1 to calculate an availability score (US _n ) for additional flying organism species.
Return to 03.

On the other hand, if the determination in step S313 is YES, since the calculation of the availability score (US _n ) for all the flying organism species has been completed, the process proceeds to step S315 and returns to the original routine.

Returning to the original routine, in the next step S203, a subroutine of a selectability score (SS _n ) calculation process is executed. FIG. 8 is a flowchart of a subroutine of a selectability score (SS _n ) calculation process.

When the selectability score (SS _n ) calculation subroutine is started in step S400, the process proceeds to step S401, and the data and map related to (green space ID) = 1 selected in the previous selection step are displayed. Obtain from the green space database.

  Next, in step S402, 1 is set to the variable n.

In step S403, the SP _{n home} area is acquired from the habitat condition database.

  In step S404, all meshes (M) in the action area are acquired. Here, it is assumed that the total number of meshes acquired is L, and each mesh is numbered 1 to L.

  In step S405, 1 is set to a variable m for counting M.

In step S406, it is determined whether or not the mesh M _{m of} interest is green.
When this determination is YES, in step S407, the proximity score (PS _m )
Is stored as 1.

If the determination in step S406 is NO, in step S408, the proximity score (PS _m ) is calculated from the reciprocal of the distance (distance raster) between the mesh M _m and the green area of interest and stored.

In step S409, it is determined whether m = L. If the determination in step S409 is NO, the proximity score (PS _m ) for all meshes in the action area
Is not calculated, the process proceeds to step S410, the variable m is incremented by 1, and the process returns to step S406. On the other hand, if the determination in step S409 is yes, the process proceeds to step S411.

In step S411, it focused to have flying organism species selectable score (SS _n
) Is calculated by (ΣPS _m ) / L and stored.

  In step S412, it is determined whether n = X.

If the determination in step S412 is NO, the process proceeds to step S413, and n is incremented by 1, and the process returns to step S403 in order to calculate a selectability score (SS _n ) for a further flying species.

On the other hand, if YES is determined in step S412, selectable score for the species in all flight organisms to (SS _n) so calculated is completed, the process proceeds to step S414, and returns to the original routine.

Returning to the original routine, in the next step S204, a subroutine of the flying possibility score (IS _n ) calculation process is executed. FIG. 9 is a flowchart of the subroutine of the flying possibility score (IS _n ) calculation process.

When the subroutine of the flying possibility score (IS _n ) calculation process is started in step S500, 1 is set to the variable n in step S501.

In step S502, the flying possibility score (IS _n ) is calculated by the product of the availability score (US _n ) and the selectability score (SS _n ) and stored.

  In step S503, it is determined whether n = X.

If the determination in step S503 is no, the process proceeds to step S504, where n is incremented by 1, and the process returns to step S502 in order to calculate a flying possibility score (IS _n ) for a further flying species.

On the other hand, if the determination in step S503 is YES, since the calculation of the flying possibility score (IS _n ) for all the flying organism species has been completed, the process proceeds to step S505 and returns to the original routine.

Returning to the original routine, in the next step S205, the flying possibility score (IS ₁ ) for all the flying organism species calculated and stored (SP ₁ , SP ₂ , SP ₃ ,... SP _X ).
, IS ₂ , IS ₃ ,... IS _X ) are output to the output unit 19 of the system.

  In the next step S206, the processing of the system is terminated.

  As described above, according to the flying possibility score calculation method and the flying possibility score calculation system using the method according to the present invention, the movement and the movement of each flying species, in particular, with the quality / distribution of green spaces that are different for each region. -Reflecting the habitat environment, it becomes possible to quantitatively predict biota with high possibility of flight, and prediction by human hands based on actual survey information becomes unnecessary.

  Next, another embodiment of the present invention will be described. In the green space database, only data created based on the remote sensing data is stored. However, in another embodiment of the present invention, in addition to the above data, a virtual space created based on the planned greening site is stored. To store the green space data. Thereby, it becomes possible to calculate the flying possibility score for each flying species in the planned greening site.

  FIG. 10 is a partially enlarged view of remote sensing data used in the flying possibility score calculation method according to another embodiment of the present invention. In FIG. 10, (green space ID) = Y is assumed as a planned greening site.

  FIG. 11 is a diagram showing an example of a data structure of a green space database used in the flying possibility score calculation method according to another embodiment of the present invention. Corresponding to (green space ID) = Y, “mesh position data” and “area” are stored, and the assumed “deciduous tree ratio”, “evergreen tree / conifer ratio”, “evergreen tree / conifer ratio”, “low” “Stem ratio” is memorized.

Based on such a green space database, the green possibility unit corresponding to the green space unit of (green space ID) = Y is selected by selecting the green space unit of (green space ID) = Y in step S201 in the flowchart shown in FIG. (IS ₁ , IS ₂ , IS ₃ ,... IS _X ) can be simulated.

  As described above, according to the flying possibility score calculation method and the flying possibility score calculation system using the method according to the present invention, as a greening effect in the development site, the flying ability with high possibility of flying before and after the project. The biota can be compared quantitatively.

DESCRIPTION OF SYMBOLS 10 ... System bus, 11 ... CPU (Central Processing Unit), 12 ... RAM (Random Access Memory), 13 ... ROM (Read Only Memory), 14 ... Communication control part, 15. ..Input control unit, 16 ... output control unit, 17 ... external storage device control unit, 18 ... input unit, 19 ... output unit, 20 ... external storage device

Claims (3)

A green space database that converts the area of each green space unit, which is a land block covered with green space, and the ratio of vegetation types into data in the target area ,
For each species of flying organisms, a habitat condition database in which a green cover area score related to the area scale, a vegetation type score related to vegetation, and a home range are converted into data ,
A selection processing unit for selecting a green space unit that is converted into data in the green space database;
An availability score calculation processing unit that calculates an availability score for each species of flying organisms based on the habitat condition database for the green space unit selected by the selection processing unit ;
For a green space unit selected by the selection processing unit , a selectability score calculation processing unit that calculates a selectability score for each species of flying organisms based on the green space database and the habitat condition database;
From the availability score calculated by the availability score calculation processing unit and the selectability score calculated by the selectability score calculation processing unit , a flying possibility score for each species of flying organisms is calculated. A flying possibility score calculation processing unit ,
The flight possibility score calculation system characterized by having. The green database, flying potential score calculation system according to claim 1, characterized in that it is constituted on the basis of the remote sensing data. Furthermore, the flight possibility score calculation system according to claim 2, wherein the green space database is configured based on virtual green space data.

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