JP2020098442A - Tree width setting device and program - Google Patents

Abstract

To provide a tree width setting device and a program for more stably and accurately identifying a vertex of a tree.SOLUTION: The tree width setting device includes a width setting unit for setting a region width of a tree based on characteristics of the tree in each of vertex candidates of the tree extracted using a numerical surface model and a numerical elevation model.SELECTED DRAWING: Figure 4

Description

The present invention relates to a tree width setting device and a program.

Conventionally, there is a technique for identifying the apex of a tree using sensing data. The identified vertices are used for various purposes related to grasping and utilizing forest resources such as, for example, counting the number of trees or establishing a felling plan for each tree.

As one of the methods to identify the vertex position of the tree, the distribution of the height from the ground related to the upper edge of the crown of the tree is acquired, and the vertex of the tree is extracted by extracting the spatial maximum point of the height in the distribution. There is a specific technology. The crown height data (Digital Canopy Height Model, DCHM) showing the distribution of height is the digital surface model (Digital Surface Model, DSM) showing the height distribution including the tree surface, and the digital elevation showing the height distribution of the ground surface. It is obtained by taking the difference from the model (Digital Elevation Model, DEM). As one of measurement methods for obtaining these DSM model and DEM model, a laser profiler using reflection of laser light emitted from the sky is known.

At this time, depending on the branching of the tree or the like, the vertices may not be properly detected and may be specified as being too small or too large. Patent Document 1 discloses a technique for obtaining distributions of the above-ground opening and the below-ground opening from DCHM data and detecting the respective vertices within a predetermined tree interval range based on these values. The tree interval is separately set by the user, for example.

JP, 2009-022278, A

However, the stand density and the distance between trees in the forest are not uniform for each tree. Therefore, in the conventional handling, there is a problem that the identification accuracy of the vertices of the tree tends to be low as a whole.

An object of the present invention is to provide a tree width setting device and a program for more stably and accurately specifying the apex of a tree.

In order to achieve the above object, the present invention provides
A tree width setting device characterized by comprising a width setting unit for setting a region width of the tree based on the characteristics of the tree in each of the tree vertex candidates extracted using the numerical surface model and the digital elevation model. is there.

According to the present invention, there is an effect that it is possible to more stably and accurately specify the apex of a tree.

It is a block diagram which shows the function structure of a processing apparatus. It is a schematic diagram explaining DSM, DEM, and DCHM. It is a figure which shows tree distribution information and geographical information typically. It is a figure explaining the outline of the flow of tree specific processing. It is a chart showing an example of a part of forest stand density table. FIG. 4 is a diagram showing an example of a list of vertex candidates and a diagram showing an example of distribution. It is a flow chart which shows the control procedure of tree vertex identification processing. It is a chart showing an example of the result of having specified the number of trees.

Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 is a block diagram showing a functional configuration of a processing device 1 which is an embodiment of a tree width setting device.

The processing device 1 is, for example, a normal computer (computer terminal or server), and includes a control unit 11, a storage unit 12, an input/output interface 13, a display unit 14, an operation reception unit 15, and the like.

The control unit 11 is a processor that performs various arithmetic processes, and includes, for example, a CPU (Central Processing Unit). The CPU performs various control processes by reading and executing programs and the like from the storage unit 12.

The storage unit 12 includes a RAM and a non-volatile memory, and stores various data. The RAM provides the CPU with a working memory space and stores temporary data. The non-volatile memory stores and holds the program 121, setting data, and the like. The nonvolatile memory may include a flash memory, or may include an HDD (Hard Disk Drive) instead of or in addition to the flash memory. Further, the initial control program and the like may be stored in a ROM (Read Only Memory). The program 121 includes a tree identification processing program described later.

The input/output interface 13 includes a connection terminal 131 for connecting to a peripheral device, a communication unit 132 for transmitting/receiving a signal to/from an external device, and the like. Examples of the peripheral device include a large auxiliary storage device such as a database device 21 and an optical reading device 22 for reading a portable storage medium (optical disk) such as a CDROM, a DVD, and a Blu-ray (registered trademark). Further, the portable storage medium may include a magnetic tape, and the peripheral device may include a reading device for reading the magnetic tape. As the connection terminal, one that conforms to various standards such as USB (Universal Serial Bus) may be used. Alternatively, the communication unit may be externally acquired via a network line such as a LAN (Local Area Network). The data stored in the database device 21 and/or the portable storage medium includes DSM data 201, DEM data 202, tree distribution information 203 (distribution information), geographical information 204 (environmental characteristic information), and a forest density table 205. included.

The DSM data 201 and the DEM data 202 associate the horizontal coordinates of the center position with the altitude (elevation value) for each of a plurality of meshes that divide the two-dimensional map with respect to the two-dimensional surface (two-dimensional map surface) parallel to the horizontal plane. It is a thing.

FIG. 2 is a schematic diagram illustrating DSM, DEM, and DCHM.
As shown in FIG. 2( a ), the altitude of DSM includes structures on the ground, forests, etc., and in the forest zone, the altitude distribution related to the upper edge of the crown is shown. These values are measured and acquired by, for example, a laser profiler. This altitude may include the transmission line W and the like, as described later. In addition, there are cases where the height of the ground or undergrowth obtained through the gap between leaves and branches is mixed. As described later, the upper limit reference height Hmax and the lower limit reference height Hmin are set in order to exclude data of heights that are not appropriate as the height of trees. The horizontal coordinate may be a latitude/longitude value, or may indicate a relative distance from a predetermined origin position.

In the DEM, the altitude of the ground surface is associated with the horizontal coordinate for each of a plurality of meshes that divide the two-dimensional map on the two-dimensional surface parallel to the horizontal plane. That is, the DEM data 202 shows the distribution of altitude values (elevation) obtained by removing the heights of structures and trees on the ground from the DSM data acquired by a laser profiler or the like. The horizontal coordinate is represented similarly to the DSM data 201. The distance D between adjacent trees is the distance D between trees.

As shown in FIG. 2B, in the forest zone, the difference between the value of the DSM data 201 and the value of the DEM data 202 at the same horizontal coordinate is the height H of the crown top edge (that is, the height of the tree at each position). ) And its distribution is DCHM. The value of the DSM data 201 and the value of the DEM data 202 are equal in a portion where there is no tree or structure and the direct ground surface is observed.

The tree distribution information 203 is information relating to the distribution of tree species. In the tree distribution information 203, for example, information on a single tree type or a dominant tree type is defined in a mesh for each unit position range. By referring to the tree distribution information 203, the tree species S at the position corresponding to the specified latitude/longitude information can be obtained. As the tree distribution information 203, a forest topographic map in which the growth range of each tree species is specified in advance by actual measurement or the like is used. Alternatively, the tree distribution information 203 may be subjected to a process of identifying a tree type in parallel with the process of extracting a vertex candidate described below. For example, the tree species identification process may be performed based on the photography data that is performed at the same time as the laser measurement of the DSM data 201 and the DEM data 202.

The geographic information 204 holds range information (geographical characteristics) of the area G, which is an area larger than the distribution of the tree species S. The area G is defined at least in a range in which environmental conditions such as climate and snow are different, for example, in the range of Hokkaido, the Sea of Japan side of Tohoku, and the Pacific side of Tohoku. In general, the area G may be divided into prefectures or municipalities, for example.

FIG. 3 is a diagram schematically showing the tree distribution information 203 and the geographical information 204.
In the tree distribution information 203 of FIG. 3A, the unit position range B has, for example, a rectangular (square) shape, and the size may be the same as the unit area in the DSM data 201 or the like, It may be a predetermined multiple of the unit area. In the forest phase diagram, areas S1, S2, S3, etc. are defined for each distribution range of tree species. The blank area is the area other than the forest. Of these, specific tree species are clearly specified for the area of single tree species. In addition, the tree type may be indicated by an ID value or the like in each mesh, and the ID value and the name of the tree type may be separately associated with each other. Further, since it is only necessary to finally be able to calculate the region width according to the tree type, if the correspondence that can be understood within the processing device 1 is made (if all the processing is possible only with the ID value, ), the name of the tree species need not be specified. Specific examples of tree species include cypress, cedar, and red pine. A plurality of types of mixed forests may be broadly defined as broad-leaved forests or coniferous forests. For example, the point P1 designated according to the latitude/longitude information of the horizontal position in DSM, DEM, DCHM, etc. belongs to the area S2 and is identified as a tree of the tree species corresponding to the area S2.

In the geographical information 204 of FIG. 3B, an area boundary BR is defined, and both sides are set as areas G1 and G2 with the area boundary BR as a boundary. Area information to which the unit position range corresponding to the position belongs is obtained based on the horizontal position information in DSM, DEM, DCHM, or the like. The unit position range may be the same as the unit position range B of the tree distribution information 203, or may be finer. Alternatively, only the portion along the boundary may be defined more finely than the other portions. For example, the point P1 is identified as belonging to the area G1. It suffices that each area G can be associated with each other when calculating the value according to the maximum density of standing trees, which will be described later. The prefecture name may be directly designated, or an ID is assigned to each area. It may have been done.

In the forest density table 205, the calculation parameter for obtaining the highest standing tree density according to the tree species S, the area G, and the average height of the upper trees of the forest is associated with the combination of the tree species S and the area G. It's a table. The stand density table 205 will be described later.

The display unit 14 causes the display screen to display menus, statuses, processing results, and the like, under the control of the control unit 11. The display screen is not particularly limited, but includes, for example, a liquid crystal display screen. When a touch panel is provided as the operation reception unit 15, the display screen is used together with the touch panel and is provided so as to overlap the touch sensor.

The operation receiving unit 15 includes, for example, an input device such as a keyboard and a mouse, and receives an input operation from the outside such as a user. The input device may include a touch panel (touch sensor) or the like.

Next, the principle of the tree identification process will be described. The tree identification process is a process of identifying the apex position of a tree within a defined area.

FIG. 4 is a diagram for explaining the outline of the flow of the tree identification processing.
Here, first, the crown height data (DCHM) is acquired by taking the difference between the digital surface model (DSM) and the digital elevation model (DEM) showing the height distribution of the ground surface (PR1). A maximum point of height is extracted from the DCHM data as a vertex candidate of the tree (PR2; candidate extraction unit). As the extraction method, for example, a conventional method of acquiring a local maximum value (maximum value) may be used.

At this time, the maximum point may occur at a place other than the tree top due to branching or the like. In the process described below, candidate points that are determined not to be vertices are removed from the extracted vertex candidates, and therefore extraction is better performed with over-detection with false detection than under-detection with missed detection. Therefore, the determination range of the local maximum value may be set narrower than the minimum distance between the tree vertices that can be actually assumed. For example, in an artificial forest, in many cases, the planting density at the time of planting has an upper limit (for example, 4000 trees/ha, etc.), and after that, it is felled or withered and monotonically decreases. Therefore, the minimum interval is set on the basis of this planting density, that is, a value that allows trees to be separated at the planting density. Here, for example, it is set to a 120 cm square or a circle having a diameter of 120 cm (radius 60 cm).

The tree distribution information 203 is referred (acquired), the tree species S of the area (region) including the position extracted as the vertex candidate is specified (PR3; type specifying unit), and the geographical information 204 is referred to. The area G at the position is specified (PR4; characteristic specifying unit).

The inter-tree distance D, which is the minimum distance between the tree vertices at the position of each vertex candidate, is obtained (PR5), and the region width R (candidate region) of the vertex candidate is thus determined. The inside of the circular area having the area width R as the diameter with the vertex of the tree as the center is set as an exclusive area that does not overlap with the circular areas of other trees. Here, based on the height H of the tree at the position of the target vertex candidate, the tree species S of the area including the tree, and the geographical area G, an expression for calculating the highest density of standing trees is used. A numerical value N corresponding to the highest density of standing trees is calculated, and the inter-tree distance D is calculated from the numerical value N. As described above, the maximum stand density is a value calculated for the stand based on the average height of the upper tree height of a forest (stand) in which trees of similar height are lined up. Here, for each vertex candidate, a numerical value N is calculated based on the height H. That is, the calculated inter-tree distance D is a variable that is individually set for each vertex candidate. The area width R is set to be equal to the inter-tree distance D.

The calculation information of the numerical value N is acquired from the stand density table 205. The maximum standing tree density depends on the tree species S, the geographical area G, and the average value Ha of the upper tree heights. Here, the artificial forest stand density control chart etc. by the Japan Forestry Technology Association are used. The maximum tree density Nr is defined by the following (Formula 1).
log(Nr)=ab×log(Ha) (Equation 1)
The coefficients a and b are numerical parameters determined by the tree species S and the area G. That is, in the stand density table 205, the values of the coefficients a and b corresponding to the combinations of the tree species S and the area G are set and stored, respectively.

FIG. 5 is a chart showing an example of a part of the stand density table 205. In the stand density table 205, the tree species S, the area G, and the coefficients a and b corresponding to these are stored in a list in association with each other. As these values, in addition to the above-mentioned artificial forest stand density control chart, original or third-party created data set in a specific region or foreign country may be used. In this case, elements other than the tree species S and the area G may be used to specify the numerical parameters (here, the coefficients a and b).

By the identified tree species S and area G, the coefficients a and b corresponding to them are acquired from the stand density table 205. The numerical value N is calculated by using the height H instead of the average value Ha of the upper tree height together with these coefficients a and b. The reciprocal (N) ^−1/2 of the square root of the numerical value N is the minimum tree distance D, that is, the region width R. Note that the calculation formula may be predetermined so that the region width R is directly obtained from the coefficients a and b and the height H. As can be seen from (Equation 1), the numerical value N decreases as the height H increases, and the region width R increases correspondingly.

A region width R indicating a range (a candidate region) that does not overlap with a candidate region of another tree around the tree based on the position information (that is, the area G) and the height H of the vertex candidates extracted as described above. Is obtained. This region width R depends on the height H even for the same tree species in the same area. Therefore, in the processing device 1 of the present embodiment, the region width R becomes a different value for each vertex candidate. There are cases.

FIG. 6 is a diagram (a) showing an example of a list of vertex candidates obtained in this way and a diagram (b) showing an example of distribution.
As shown in FIG. 6A, the height H, the tree species S (characteristics of trees), and the area G (geographical characteristics) are defined for each of the vertex candidates represented by latitude and longitude. A region width R when the vertex candidate is a vertex is set according to the inter-tree distance D obtained based on these, and is associated as a region related to the vertex candidate (candidate region) (PR6; width setting unit). (May include PR5)).

The vertices are specified based on the setting data of this candidate area (PR7; tree vertex specifying unit).
As shown in FIG. 6B, when a range (candidate region) whose diameters are the region widths (R11 to R14) centered on the vertex candidates (P11 to P14) is set, the candidate region is It is detected whether or not it overlaps with the candidate area defined for the vertex candidate. Here, the candidate area of the vertex candidate P11 and the candidate area of the vertex candidate P12 overlap. If they overlap, it is determined that one of the vertex candidates is erroneously extracted. Therefore, one of the vertex candidates is removed and the remaining vertex candidate is specified as a vertex so that the overlapping range is eliminated.

When there is an overlap, which vertex candidate is to be removed may be mechanically determined, or may be determined manually by a user. In the case of performing mechanically, for example, a point having the largest height H is selected from a plurality of overlapping points (here, the vertex candidate P12 is higher than the vertex candidate P11). When the user makes a determination, the overlapping situation may be displayed on the display unit 14, and the user may perform the operation of selecting one or removing other than one by the operation reception unit 15 by viewing the display content.

The vertex position is specified by performing a confirming operation as necessary. The specified vertex position data is directly and/or statistically processed to be displayed on the display screen of the display unit 14, transmitted to the database device 21 and/or an external device, or an optical reading device. The optical disc set to 22 may be writable.

FIG. 7 is a flowchart showing a control procedure by the control unit 11 of the tree vertex specifying process executed by the processing device 1 of the present embodiment. This process is started, for example, when the user specifies a predetermined position range from the operation reception unit 15 and issues a start command.

When the tree vertex specifying process is started, the control unit 11 acquires the DSM data 201 and the DEM data 202 corresponding to the designated area (step S101). The control unit 11 acquires the elevation value at each position of the DSM data 201 and the elevation value at each position of the DEM data 202, calculates a difference value, and generates DCHM data in association with the horizontal coordinates (step). S102).

The control unit 11 detects the maximum value (maximum value) based on a predetermined local range and extracts it as a vertex candidate (step S103). The control unit 11 excludes out-of-reference data from the extracted vertex candidates (step S104). The out-of-reference data is, for example, out of the reference height range. For example, as described above, the power transmission line is detected at a height H higher than the upper limit reference height (eg, 30 m), and the undergrowth or the like is detected at a height H less than the lower limit reference height (eg, 5 m). It should be noted that the process of step S104 may be performed before the process of step S103 to exclude data outside the reference in advance.

The control unit 11 sequentially selects unselected ones from the extracted vertex candidates (step S105). The control unit 11 uses DCHM to acquire the height H at the position of the selected vertex candidate (step S106).

The control unit 11 refers to the tree distribution information 203 and identifies the tree type S corresponding to the horizontal coordinates of the vertex candidates (step S107). The control unit 11 refers to the geographic information 204 and specifies the area G corresponding to the horizontal coordinates of the vertex candidates (step S108). The control unit 11 identifies the coefficients a and b corresponding to the tree species S and the area G, and calculates the area width R using these coefficients a and b and the height H (step S109).

The control unit 11 determines whether or not all the vertex candidates have been selected (step S110). When it is determined that there is an unselected vertex candidate (“NO” in step S110), the process of the control unit 11 returns to step S105. When it is determined that all the vertex candidates have been selected (“YES” in step S110), the process of the control unit 11 moves to step S111.

When shifting to the processing of step S111, the control unit 11 sets, as a candidate area, an area centered on the position (horizontal coordinate) of each vertex candidate and having the radius of the area width R obtained for the vertex candidate. (Step S111; width setting step, width setting means (may include step S109)). The control unit 11 determines whether or not there is an overlapping portion between the set candidate areas (step S112). When it is determined that there is no overlapping portion (“NO” in step S112), the process of the control unit 11 moves to step S116.

When it is determined that there is an overlapping portion (“YES” in step S112), the control unit 11 causes the display unit 14 to display overlapping vertex candidates (step S113). The display may be performed on the entire processed image (the control unit 11 can make the user easily perceived by changing the color of only the overlapping portion) or by enlarging only the overlapping portion. May be done.

The control unit 11 determines whether or not an operation of selecting one of the overlapping portions is detected (step S114). When it is determined that it has not been detected (“NO” in step S114), the control unit 11 repeats the process of step S114.

When it is determined that the operation of selecting one is detected (“YES” in step S114), the control unit 11 removes all unselected vertex candidates (step S115). Then, the processing of the control unit 11 moves to step S116.

When the processing shifts from the processing of steps S112 and S115 to the processing of step S116, the control unit 11 determines the remaining vertex candidates as the vertices of the tree (step S116). Then, the control unit 11 ends the tree vertex specifying process.

As described above, when one vertex candidate is mechanically and automatically selected from the overlapping area without using the manual selection, the above selection process is appropriately performed instead of the processes of steps S113 and S114. It is only necessary to remove the unselected vertex candidates.

As described above, the processing device 1 of the present embodiment includes the control unit 11, and the control unit 11 serves as the width setting unit and the tree in each of the tree vertex candidates extracted using the DSM model and the DEM model. Based on the characteristics, that is, the height H and the tree species S of the tree, the region width R of the tree is set.
In this way, since the region width R is quantitatively evaluated and set for each tree, the region width R can be easily and more stably determined with high accuracy. By using this region width R, it becomes possible to specify the apex of the tree more stably and accurately.
Further, it is not necessary to perform trial and error on the optimum value of the minimum tree width or to require the experience of reading the standing tree density by the user when the apex of the tree is specified. In particular, read the standing tree density in a specific range of vertex positions in advance and set it before starting the process, or once define a vertex candidate and then read the surrounding tree densities and perform the process again. Since it does not require a double labor, it can contribute to the identification of the vertex position of the tree more efficiently and accurately.

The tree characteristics include the height H of the tree at the position of the vertex candidate. Even trees that were planted at the same time in the same stand may have different heights depending on the growth conditions such as topography. Further, if the trees are planted at different times, a larger height difference may occur. According to these, since the area width R of the tree is determined using the height H of the tree at the position of the vertex candidate, the area width R is determined more appropriately for each tree, and the excess area around the tree is determined. It is possible to avoid specific identification and exclusion of trees beyond necessity.

The control unit 11 also identifies, as a type identifying unit, the tree species at each of the positions of the vertex candidates. The tree characteristics related to the calculation of the region width R include the specified tree species.
Thereby, it is possible to appropriately cope with the difference in the shape of the crown of the target tree and to determine the more accurate region width R. This makes it possible to identify each tree vertex more appropriately and avoid excessive identification or exclusion.

Further, the control unit 11, as a type specifying unit, acquires the tree distribution information 203 and specifies the tree type by the information of the tree type S of the area including the position of the vertex candidate in the tree distribution information 203. That is, since the tree species at the corresponding position are acquired by referring to the tree distribution information 203 that is determined in advance, it is possible to easily specify the tree species and determine the appropriate area width R.

Further, the control unit 11 as the characteristic specifying unit acquires the geographical information 204 and specifies the environmental characteristic information based on the information of the area G of the area including the position of the vertex candidate in the geographical information 204. The control unit 11 as a width setting unit sets the region width R based on the environmental characteristic information. That is, the control unit 11 determines the region width R in consideration of not only the characteristics of the tree but also the growth environment of the tree. Thereby, the region width R of each tree can be determined more accurately.

The environmental characteristic information also includes the geographical characteristic of the position of the vertex candidate. That is, the region width R can be set more appropriately by reflecting the climatic conditions according to the geographical area. In addition, since information on geographical areas such as local areas and prefectural governments can be used relatively easily, it is possible to more reliably identify the apex of a tree based on an appropriate area width R with less effort. it can.

Further, the control unit 11 as a width setting unit calculates a value based on the characteristics of the tree using an expression for obtaining the highest density of standing trees, and sets the region width R according to the value. The maximum tree density corresponds to the minimum distance between trees. The tree-to-tree distance is the sum of the radius r indicating the range of influence between the tree and the tree adjacent to the tree, so that the area width R is obtained from the tree-to-tree distance. Therefore, by obtaining the value corresponding to the maximum standing tree density using the height H of each tree, it is possible to more finely determine the region width R of each tree within the assumed minimum range. This makes it possible to exclude duplicate candidates more accurately when identifying the vertices of a tree, and to avoid unnecessary elimination.

Further, the DSM model and the DEM model are calculated using a laser profiler. In the forest zone, since the DSM model and the DEM model are simultaneously acquired with good accuracy through the trees with gaps, it is possible to efficiently acquire the DCHM model. Therefore, it is possible to extract an appropriate vertex candidate and the height of the vertex candidate. It is possible to specify H. Therefore, in the processing device 1, it is possible to calculate the region width R with higher accuracy, and accordingly, it is possible to more accurately specify the vertex.

Further, the control unit 11 serves as a candidate extraction unit and extracts a vertex candidate of the tree using the DSM model and the DEM model. As a result, the processing apparatus 1 can extract the vertex candidate and determine an appropriate region width R for more appropriately determining whether or not the extracted vertex candidate is a vertex.

Further, in the processing device 1 as the embodiment of the tree vertex specifying process, the control unit 11 controls each functional operation as the above-described tree width setting device and each tree determined by the set region width R as the tree vertex specifying unit. The process of identifying the vertices from the vertex candidates is performed so that the areas of No. That is, if the regions (the circular regions having the diameter of the region width R with the vertex of the position of the apex candidate as the apex) overlap each other in the apex candidates having the aforesaid region width R, one of the apex candidates Is determined to be an erroneous detection, and only one of the overlapping areas is selected. Since the area defining this overlapping area is made more appropriate by setting the above-described area width R, the processing device 1 can more accurately determine the overlapping and specify the vertices.

Further, by the above-described processing, the region width R can be determined easily and more stably and with high accuracy as in the processing device 1 described above. By using this region width R, it becomes possible to specify the apex of the tree more stably and accurately.

Further, the program 121 of the present embodiment causes the computer (processing device 1) to determine the region width R of the tree based on the characteristics of the tree in each of the tree vertex candidates extracted using the DSM model and the DEM model. Make it function as a width setting means.
By installing the program 121 that performs such processing and causing the computer to perform the processing, it is possible to stably and appropriately determine the region width R with higher accuracy by software. As a result, it becomes possible to specify the vertex of the tree more stably and accurately.

The present invention is not limited to the above embodiment, and various modifications can be made.
For example, in the above-described embodiment, the area width R is calculated based on the formula for obtaining the maximum standing tree density Nr using the height H, the tree species S, and the area G, and the original data and the third party data are used. However, the representative value (average value) of the calculation parameters of all regions related to the specific tree species S may be acquired and transformed so that the calculation can be performed without the information of the area G. .. The processing load can be simplified by slightly reducing the load of data reading and processing. Similarly, even if one of the height H and the tree species S in the calculation is omitted (even if it is a fixed value), the accuracy can be slightly improved as compared with the conventional one.

Further, in this embodiment, each data of the DSM data 201, the DEM data 202, the tree distribution information 203, the geographical information 204, and the stand density table 205 is assumed to be acquired from the external database device 21 or the portable storage medium. As described above, the processing device 1 may hold some or all of them in advance.

Further, in the above-described embodiment, the detection of the local maximum value of DCHM is used to extract the vertex candidates, but the present invention is not limited to this. Other mechanically extractable methods may be used or used together, or data from which noise has been removed from DCHM data may be used as it is.

Further, in the above-described embodiment, the geographical information 204 as the environmental characteristic information is described as an example of division into areas such as prefectures and the Pacific side/Japan Sea side of regions as information for dividing areas according to environmental conditions. However, it is also possible to make a finer division within the prefecture depending on the snow cover situation, for example, the north slope or the south slope. Alternatively, information on the snow cover status and data for numerical correction thereof may be held so that the above-mentioned classification is corrected based on the snow cover status.

In the above embodiment, the DSM data 201 and the DEM data 202 are described as being acquired by the laser profiler, but the present invention is not limited to this. For example, the altitude distribution may be acquired by stereo matching using aerial photographs taken at different positions.

Further, in the above-described embodiment, it has been described that each process related to the extraction of candidates, the setting of the area width, and the specification of the vertices is performed by a single processing program (tree specifying processing program). And a part of each of these processes may be divided into different programs, created, and called and executed separately.

Further, a part of software-like processing relating to the tree identification processing program may be performed by hardware such as a dedicated logic circuit.

Also, each of these processes may be distributed to different processing devices (computers).

In addition, the specific details such as the device configuration, the processing content and the processing procedure shown in the above-described embodiment can be appropriately changed without departing from the spirit of the present invention.

In the above embodiment, the program 121 is described as being stored in the non-volatile memory or the HDD of the storage unit 12, but the present invention is not limited to this. The program 121 may be stored in a portable storage medium such as an optical disc such as a CDROM, a DVD, or a Blu-ray. A carrier wave is also applied to the present invention as a medium for providing the data of the program according to the present invention via a communication line.

[Example]
Regarding sugi and cypress forests in the Kita Kinki and Chugoku regions, sugi and/or cypress are used as tree species (in some areas, sugi and cypress coexist, and sugi is dominant), and radii with different stand density The number of trees actually measured visually by setting a circular area of 11.28 m was compared with the number of trees mechanically specified.

In the case where the present embodiment is applied to each of these circular areas, first, a local maximum value is obtained by using an area setting with a radius of 60 cm, and the vertex candidates of the tree are extracted. Next, the minimum tree-to-tree distance D was calculated using the coefficients a and b of the highest standing tree density obtained by applying the above area and main tree species and the height H at the vertex candidate. Then, when the obtained inter-tree distance D is the area width R with the vertex candidate as the center and a circle area having the diameter of the area width R is defined as the candidate area, when there is an overlap of the candidate areas between the vertex candidates. Removed candidate vertices until there was no overlap.

On the other hand, as a comparative example, the vertex candidate extracted by setting the radius r of the area for obtaining the local maximum value as a fixed value of 60 cm to 180 cm was directly specified as the tree vertex.

FIG. 8 is a chart showing an example of the result of specifying the number of trees.
In the set 20 circular areas, the actual number obtained by visual observation is distributed between 17 and 67. That is, there is a large difference in the distance between trees. On the other hand, when the radius r=60 cm, the specified number is larger than the number of eyes, and is often specified excessively. The root mean square error (RMSE) at this time is 12.2.

When the radius r becomes larger than 60 cm, the specific number decreases. When the radius D is 100 cm, the specific number is often smaller than the visual number, and it has already been detected as being undersized. The RMSE is 10.2, which is slightly smaller than that when the radius r is 60 cm. When the radius r is 140 cm and 180 cm, the number of detections is further reduced, and the RMSE is 17.8 and 22.1.

On the other hand, in the present embodiment, the number of visual observations is not biased toward over-detection or under-detection, and the deviation is suppressed as a whole. The RMSE is 4.6, which is sufficiently smaller than the radius r=60 cm and 100 cm in the comparative example. Looking at each case, the specific number in any of the comparative examples may be closer to the visual number than the specific number in the example, but in the present example, the deviation amount as a whole is stably suppressed to be small. It has been shown that

Further, when the tree species are classified into an area of cedar (8 areas) and an area of cypress (10 areas), the number of trees visually observed is 17 to 67 for cypress and 26 to 49 for hinoki. Then, the RMSEs of Japanese cedar and Japanese cypress are stably maintained at small values of 5.0 and 4.7, respectively. On the other hand, at r=60 cm in the comparative example, the RMSE value for cypress (16.7) is larger than the RMSE value for cedar (4.1). On the contrary, at r=100 cm, the value of RMSE for cedar (12.8) is larger than the value of RMSE for cypress (8.5). That is, in the present example, the result was obtained that the difference depending on the tree species was flexibly dealt with, and more stable and accurate identification was performed.

1 Processing Device 11 Control Unit 12 Storage Unit 121 Program 13 Input/Output Interface 131 Connection Terminal 132 Communication Unit 14 Display Unit 15 Operation Receiving Unit 21 Database Device 22 Optical Reading Device 201 DSM Data 202 DEM Data 203 Tree Distribution Information 204 Geographic Information 205 Forest Concentration table D Distance between trees G Area R Region width S Tree species

Claims (5)

A tree width setting device comprising a width setting unit that sets a region width of the tree based on the characteristics of the tree in each of the tree vertex candidates extracted using the numerical surface model and the digital elevation model. The tree width setting device according to claim 1, wherein the characteristics include the height of the tree at the position of the vertex candidate. A type specifying unit that specifies a tree type at each of the positions of the vertex candidates,
The type identification unit acquires distribution information of tree species, and identifies the tree species by information of the tree species of the area including the position of the vertex candidate in the distribution information,
The tree width setting device according to claim 1 or 2, wherein the characteristic includes the identified tree species. A characteristic specifying unit for specifying environmental characteristic information of the position of the vertex candidate,
The environment characteristic information includes a geographical characteristic of the position of the vertex candidate,
The tree width setting device according to any one of claims 1 to 3, wherein the width setting unit sets the region width based on the environmental characteristic information. Computer,
A program that functions as a width setting unit that sets the area width of a tree based on the characteristics of the tree in each of the tree vertex candidates extracted using the numerical surface model and the digital elevation model.

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