JP2019185449A - Forest resource information calculation method and forest resource information calculation device - Google Patents

Abstract

To calculate forest resource information with high accuracy even in forests in which there are differences in timber density, tree height, and crown size.SOLUTION: A forest resource information calculation method includes: a survey target forest area image data creation processing step (step S30) of creating survey target forest area image data on the basis of 3D point group data and geographical information; crown height image data creation processing step (step S40) of creating crown height image data representing the height of the crown of each tree existing in a survey target forest area as the crown height; and hierarchical tree vertex extraction processing step (step S50) of extracting, for each hierarchy, tree vertices of crowns in which the tree vertices are present in respective hierarchies including a first hierarchy, when the first hierarchy is defined as a hierarchy in which the maximum crown height is present among crown heights obtained from the crown height image data, and a plurality of hierarchies having a predetermined hierarchy width from the maximum crown height are set from the first hierarchy to a ground surface.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a forest resource information calculation method and a forest resource information calculation device for creating forest resource information related to a survey target forest area based on image data corresponding to a region including the survey target forest area.

  Information on forest resources including tree height (referred to as forest resource information) is important basic information in forest management. Conventionally, forest resource information is based on ground surveys where a researcher sets up a small survey area at a location that appears to be a standard forest and measures the position, diameter, and height of trees in the survey area. A method for estimating the entire forest resource information by multiplying the result by the management area is widely used.

  In such a forest resource information estimation method, it is necessary to increase the number of on-site survey points in order to increase the accuracy of forest resource estimation. Tree height calculations, which are most likely to cause errors in ground surveys, are often incapable of seeing the tops of trees from the ground because there are many obstacles and branches in the forest. Sample trees such as forest edges and isolated trees Is used to measure the tree height, create a regression equation from the relationship between the measured breast height diameter and the sample tree height, estimate the tree height, and interpolate. Furthermore, measurement errors increase when affected by the topography of the measurer's skill and measurement slope.

  Therefore, recently, tree vertices and tree extraction are also evaluated from three-dimensional point cloud data obtained by aeronautical laser measurement. In addition, an image obtained by photographing a forest from the sky with a drone, an aircraft, or the like is converted into three-dimensional point cloud data to evaluate tree vertices and tree extraction.

Japanese Patent No. 4946072 Japanese Patent No. 4279894 Japanese Patent No. 5507418

  The techniques described in Patent Document 1 and Patent Document 2 use a spatial filtering method called a known local maximum value filter to determine the vertex and tree position of a tree from three-dimensional point cloud data. × 5 etc.) is a method in which the tree vertices having the maximum value in the region are extracted by batch processing while moving the filter of the crown high point cloud data.

  The techniques described in Patent Document 1 and Patent Document 2 are effective methods for artificial forests partitioned like a board and simple and sparse conifers, but most forests have different tree density differences. And the height of the tree and the size of the canopy are intermingled, so “unextracted or mis-extracted increases”, “depending on the size of the filter, small and large canopies cannot be extracted at the same time,” “standing tree density In high forests and collective trees, only one tree height can be extracted and there are many unextracted trees. ”“ In the gap of the forest canopy, the vegetation in the lower layer is mis-extracted as the top of the tree ”. There are many issues.

  On the other hand, the technique described in Patent Document 3 is a method for detecting a tree position by projecting a three-dimensional point cloud data of a forest acquired by aerial laser measurement onto the ground to detect a tree position. In addition, there is a problem that it is difficult to classify forests in which the sizes of tree crowns are mixed into single trees.

  As described above, in the above Patent Documents 1 to 3, in the forest where the difference in the density of trees, the height of the tree and the size of the crown are mixed, the tree height, the breast height diameter of each tree, and the volume obtained from these tree height and the breast height diameter, etc. There is a problem that forest resource information cannot be calculated with high accuracy. In this specification, “tree canopy” refers to a portion composed of branches and leaves of each tree when the trees constituting the forest are viewed from above.

  Therefore, the present invention has been made to solve the above-described problems, and a forest resource information calculation method capable of calculating forest resource information with high accuracy even in a forest in which a difference in stand density, a tree height, and a crown size are mixed, and It aims at providing the forest resource calculation information setting device.

  [1] The forest resource information calculation method of the present invention is a forest resource information calculation method for creating forest resource information related to a survey target forest area, and includes three-dimensional point cloud data corresponding to an area including the survey target forest area; A survey target forest area image data creation processing step of creating survey target forest area image data based on geographical information; and a crown of each tree existing in the survey target forest area based on the survey target forest area image data A crown height image data creation processing step for creating crown height image data representing the height of the canopy as a crown height, and among the crown heights obtained from the crown height image data, a hierarchy having a maximum crown height is defined as a first hierarchy. When a plurality of hierarchies having a predetermined hierarchy width from the maximum tree crown height are set toward the ground surface from the first hierarchies, tree vertices exist in each hierarchy including the first hierarchies. That the tree vertexes of the crown, and having, respectively, the hierarchical tree vertex extraction step of extracting for each of the hierarchies.

  Thus, in the forest resource information calculation method of the present invention, based on the survey target forest area image data created based on the three-dimensional point cloud data corresponding to the area including the survey target forest area and the geographical information. The crown height image data representing the crown height of a tree existing in the forest area to be surveyed is created, and among the crown heights obtained from the crown height image data, the hierarchy where the maximum crown height exists is defined as the first hierarchy. The tree vertices of a tree crown having tree vertices in each hierarchy including one hierarchy are extracted for each hierarchy.

  Thereby, according to the forest resource information calculation method of the present invention, the crown corresponding to each tree can be extracted with high accuracy regardless of the height of the survey target tree existing in the survey target forest area. Forest resources information can be calculated with high accuracy even in forests with mixed density, tree height and crown size.

  [2] In the forest resource information calculation method of the present invention, the canopy height image data creation processing step includes meshing digital surface layer model data and digital elevation model data based on the survey target forest area image data. It is preferable to create the crown height image data in which the crown height is obtained for each mesh by creating the difference between the digital surface model data and the digital elevation model data for each mesh.

  Thereby, the crown height (the height of the crown from the ground surface) can be calculated with high accuracy for each mesh. In this case, the digital surface model data refers to data representing the altitude of a surface layer such as a tree, and the digital elevation model data refers to data representing the altitude of the ground surface. Therefore, the crown height (the height of the crown from the ground surface) can be obtained for each mesh by taking the difference between the digital surface model data and the digital elevation model data.

  [3] In the forest resource information calculation method of the present invention, each of the tree-level tree vertex extraction processing steps includes each of the first hierarchies using the crown height image data for which the crown height is obtained for each mesh. Hierarchical crown high point cloud data corresponding to individual crowns present in the hierarchy is created by the hierarchical crown high point cloud data creation processing step for each hierarchy and the hierarchical crown high point cloud data creation processing step for each hierarchy. The maximum value of the tree-based crown high point group data corresponding to each individual crown is calculated for each layer from the tree-based crown high point group data corresponding to the individual tree crown, and the calculated maximum value is calculated. It is preferable that a tree apex calculation processing step as a tree apex of a tree corresponding to the hierarchical tree crown high point group data is included.

  As described above, in the forest resource information calculation method of the present invention, tree-specific crown high point cloud data corresponding to each tree crown existing in each hierarchy including the first hierarchy is created for each hierarchy, and created for each hierarchy. The maximum value of the tree-based crown high point group data corresponding to each individual crown is calculated from the tree-based crown high point group data corresponding to each individual tree crown. Here, the crown high point cloud data for each hierarchy is created for each hierarchy, and the crown high point cloud for each hierarchy corresponding to each canopy from the crown high point cloud data for each hierarchy created for each hierarchy. The process of calculating the maximum value of the data is, for example, by creating individual tree crown high point cloud data by dividing the individual tree canopy into layers like CT (Computed Tomography) scan, This is a process of calculating the maximum value from the obtained tree-based crown high point cloud data. By performing such processing, the maximum value of each tree crown existing in each hierarchy can be calculated for each hierarchy. Note that the “tree apex of the tree crown” calculated for each hierarchy is the tree apex of the tree having the tree crown, and the tree height of the tree can be obtained from the calculated tree apex.

  [4] In the forest resource information calculation method of the present invention, when calculating the maximum value, the tree vertex calculation processing step includes, among the tree-specific crown high point group data corresponding to the individual tree crowns, It is preferable to calculate the maximum value by excluding the hierarchical crown high point cloud data corresponding to the same crown as the crown for which the hierarchical crown high point cloud data has already been created.

  Thereby, it is possible to calculate the maximum value of only the canopy in which the tree vertex is present in the current processing target layer among the layers. For example, when the current processing target hierarchy is the second hierarchy, the maximum value of the tree crown having a tree vertex in the hierarchy higher than the second hierarchy (first hierarchy) is erroneously calculated as the tree vertex of the hierarchy. Can be prevented. Thereby, it is possible to calculate the maximum value of only the crown having a tree vertex in the current processing target hierarchy (for example, the second hierarchy).

  [5] In the forest resource information calculation method of the present invention, in the tree-specific tree vertex extraction processing step, each mesh of the crown height image data is set as a processing target mesh, and an averaging filter is superimposed on the processing target mesh. At the same time, the crown height of each mesh included in the averaging filter is used as the crown height of the processing target mesh to create the crown height image data from which noise has been removed. An averaging process step is further included, and the step-by-layer crown high point cloud data creation processing step is configured to obtain, for each mesh, the crown height image data from which noise has been removed created by the averaging process step. It is preferable to create the tree-based crown high point cloud data by using the obtained crown height image data

  By performing such an averaging process, for example, when there is an extreme unevenness (such as a part where branches and leaves are sparse compared to the surroundings or a long branch), this is removed as noise. It is possible to create canopy height image data from which noise has been removed. Then, the step-by-level crown high point cloud data creation processing step uses the crown-high image data from which noise has been removed as the crown height image data to create the tree-by-layer crown high point cloud data, thereby providing a high-accuracy crown height point group data. Point cloud data can be created. In addition, a primate branch and leaf refers to a portion of the tree crown where the branches and leaves protrude from the surrounding branches and leaves.

  [6] In the forest resource information calculation method of the present invention, the layer width is preferably set to a length in the range of 0.2 m to 2.0 m.

  Hierarchy width is not particularly limited, but if it is set too fine (shorter length), the number of layers increases, the number of processing increases, the amount of data increases, and computation takes time. On the other hand, if it is set too large (length is increased), there is a problem that the accuracy of the tree-based crown high point group data is lowered. For this reason, it is preferable to set the stratum width within a range of 0.2 m to 2.0 m. For example, strata widths of about 0.4 m, 0.8 m, and 1.0 m can be exemplified. It is preferable to set in consideration of the height of trees existing in the tree.

  [7] In the forest resource information calculation method according to the present invention, the three-dimensional point cloud data is obtained from three-dimensional shape restoration technology based on photographed image data obtained by photographing images while overlapping and photographing the photographing locations from above. It is preferable that it is created by SFM (Structure from Motion).

  By using SFM, which is a three-dimensional shape restoration technique, a map-corrected ortho image without distortion can be created from two-dimensional captured image data. Then, based on the ortho image created in this way, three-dimensional point cloud data representing the height can be created.

  [8] In the forest resource information calculation method of the present invention, the three-dimensional point cloud data may be created based on laser measurement data obtained by irradiating laser light from above.

  As described above, the three-dimensional point cloud data can also be created from the laser measurement data obtained by irradiating the laser beam from the sky.

  [9] In the forest resource information calculation method of the present invention, based on the crown apex of each tree extracted for each hierarchy, area division is performed to divide an adjacent tree crown from the crown height of the crown. Preferably, the method further includes a precise crown image data creation processing step of creating precise crown image data in which individual crowns are segmented using an algorithm.

  By using such an area division algorithm, it is possible to create precise canopy image data (precise canopy image data) in which adjacent canopies are divided into individual canopies. The precise canopy image data created in this way becomes precise canopy image data from which individual canopies corresponding to the trees to be investigated can be extracted with high accuracy. Therefore, the precise canopy information created based on the precise canopy image data becomes highly accurate canopy information corresponding to each tree of the survey target tree, and based on the precise canopy information, information on forest resources (forest resource information ) To obtain highly accurate forest resource information.

  [10] In the forest resource information calculation method of the present invention, it is preferable that the region division algorithm is a Watershed algorithm.

  The Watershed algorithm is an algorithm that can extract the boundaries of individual objects with high accuracy and classify the individual objects when a large number of adjacent dense objects exist in a connected state. Therefore, by using the Watershed algorithm, a densely existing canopy can be classified with high accuracy as individual canopies regardless of the size of the canopy (crown diameter or height).

  [11] In the forest resource information calculation method of the present invention, a precise canopy information creation process for creating precise canopy information corresponding to each tree from the precise canopy image data created by the precise canopy image data creation processing step. And a forest resource information calculation processing step of creating forest resource information related to the forest area to be investigated based on the precise crown information corresponding to each tree created by the precise crown information creation processing step. The precise crown information corresponding to each tree includes at least the label number of the crown corresponding to each tree, the crown position of the crown corresponding to each tree, the crown diameter of the crown corresponding to each tree, and the The crown area of the crown corresponding to each tree is included, and the forest resource information calculation processing step includes a human being in each tree. Chest height diameter calculation processing step for calculating the diameter of the chest height as the chest height diameter of each tree, a material integration determination processing step for calculating the volume of each tree, the breast height diameter of each tree, the volume of each tree, Forest resource information aggregation processing step for performing aggregation processing of tree heights obtained from the accurate tree crown information created in the precise tree crown information creation processing step and the tree vertices extracted in the tree-specific tree vertex extraction step And are preferably included.

  The precise canopy information created based on the precise canopy image data becomes highly accurate canopy information corresponding to each tree of the investigation object tree. Therefore, the forest resource information calculation processing step calculates forest resource information (forest resource information) based on the accurate tree crown information created based on the accurate tree crown image data, thereby obtaining highly accurate forest resource information. be able to. As a result, it is possible to provide highly accurate forest resource information with little measurement error and high objectivity in the entire forest area to be surveyed, for each subgroup, and in any range, so it can be used for forest management such as forest management planning such as thinning. . Specifically, for example, the calculation result calculated in the forest resource information calculation processing step is registered in the attribute database as the forest resource summary information indicating the outline of the forest resource, and the forest resource summary registered in the attribute database is processed. By displaying the information as a list on the display, it is possible to obtain an overview of the forest resources in the forest area to be investigated.

  [12] A forest resource information calculation apparatus according to the present invention is a forest resource information calculation apparatus that creates forest resource information related to a forest area to be surveyed, and includes three-dimensional point cloud data corresponding to an area including the forest area to be surveyed. A survey target forest area image data creation unit that creates the survey target forest area image data based on geographical information, and each tree existing in the survey target forest area based on the survey target forest area image data A crown height image data creation unit for creating crown height image data that represents the height of the crown as a crown height, and of the crown heights obtained from the crown height image data, a hierarchy in which the maximum crown height exists is defined as a first hierarchy. When a plurality of hierarchies having a predetermined hierarchy width from the maximum tree crown height as a starting point are set toward the ground surface from the first hierarchy, tree crowns having tree vertices in each hierarchy including the first hierarchy Tree Point a, and having a a hierarchical tree vertex extraction unit which extracts respectively for each of the hierarchies.

  [13] In the forest resource information calculation apparatus of the present invention, the crown height image data creation unit creates meshed digital surface model data and digital elevation model data based on the survey target forest area image data Preferably, the crown height image data in which the crown height is obtained for each mesh is created by taking the difference between the digital surface model data and the digital elevation model data for each mesh.

[14] In the forest resource information calculation apparatus of the present invention, the hierarchical tree vertex extraction unit includes:
Using the crown height image data for which the crown height is obtained for each mesh, the tree-specific crown high point cloud data corresponding to individual crowns existing in each hierarchy including the first hierarchy is created for each hierarchy. A tree-specific crown high point group data creation unit and a tree-specific crown high point group data creation unit created by the tree-specific crown high point group data creation unit, A tree apex calculation unit that calculates the maximum value of the crown high point group data for each hierarchy, and uses the calculated maximum value as the tree apex of the crown corresponding to the tree-based crown high point group data. Preferably it is.

  [15] In the forest resource information calculation apparatus of the present invention, the tree apex calculation unit calculates the maximum value, among the tree-specific crown high point group data corresponding to the individual tree canopies, It is preferable to calculate the maximum value by excluding the hierarchical crown high point cloud data corresponding to the same crown as the crown for which the hierarchical crown high point cloud data has already been created.

[16] In the forest resource information calculation apparatus of the present invention, the hierarchical tree vertex extraction unit includes:
Each mesh of the crown height image data is set as a processing target mesh, an averaging filter is superimposed on the processing target mesh, and an average value of the crown height of each mesh included in the averaging filter is determined as a crown height of the processing target mesh. Is further included in the averaging processing unit for creating noise-removed crown height image data by sequentially performing the processing in each processing target mesh, the hierarchical crown height point group data creation unit, It is preferable that the tree-based crown height image data created by the averaging processing unit is used as the crown height image data for which the crown height is obtained for each mesh, and the tree-based crown height point group data is created.

  [17] In the forest resource information calculation apparatus of the present invention, it is preferable that the hierarchical width is set to a length in the range of 0.2 m to 2.0 m.

  [18] In the forest resource information calculation apparatus according to the present invention, the 3D point cloud data is obtained from a 3D shape restoration technique based on captured image data obtained by overlapping and capturing images from above. It is preferable that it is created by SFM (Structure from Motion).

  [19] In the forest resource information calculation apparatus of the present invention, the three-dimensional point cloud data may be created based on laser measurement data obtained by irradiating laser light from above.

  [20] In the forest resource information calculation apparatus according to the present invention, based on tree vertices extracted from the respective hierarchies, area division is performed to divide adjacent tree crowns from the crown height of the crown. It is preferable to further have a precise canopy image data creation unit for creating precise canopy image data in which individual canopies are segmented using an algorithm.

  [21] In the forest resource information calculation apparatus of the present invention, it is preferable that the region segmentation algorithm is a Watershed algorithm.

  [22] In the forest resource information calculation apparatus of the present invention, a precise crown information creation unit that creates precise crown information corresponding to each tree from the precise crown image data created by the precise crown image data creation unit; A forest resource information calculation unit that creates forest resource information related to the forest area to be investigated based on the precise canopy information corresponding to each tree created by the precise canopy information creation unit, and The precise crown information corresponding to the tree includes at least the label number of the crown corresponding to each tree, the crown position of the crown corresponding to each tree, the crown diameter of the crown corresponding to each tree, and the corresponding tree. The forest resource information calculation unit calculates the diameter of the breast height of each person as the breast height diameter of each tree. Chest height diameter calculation unit, a material integration determination unit for calculating the volume of each tree, the chest height diameter of each tree, the volume of each tree, the precise crown information created by the precise crown information creation unit and the hierarchy It is preferable that a forest resource information totaling unit that performs a process of totaling tree heights of trees obtained based on the tree apexes extracted by the tree apex extracting unit is included.

  In addition, in the forest resource information calculation apparatus of the present invention described in [12] to [22], the same effect as that obtained by the corresponding forest resource information calculation method of the present invention described in [1] to [11] is obtained. The effect is obtained.

It is a flowchart shown in order to demonstrate the forest resource information calculation method which concerns on embodiment. It is a figure which shows the forest area image of the investigation object displayed on the display. It is a crown height image displayed on the display. It is a flowchart explaining in detail the tree-specific tree vertex extraction process (step S50) shown in FIG. It is a figure which shows 1st hierarchy tree crown high point group area | region A1, A2, A3, ... displayed on the display. FIG. 6 is a diagram illustrating tree vertices a1, a2, a3,... Extracted in the first hierarchical tree crown high point group regions A1, A2, A3,. It is a figure which shows 2nd hierarchy tree crown high point group area | region B1, B2, B3, ... displayed on the display. It is a figure which shows the 3rd hierarchy tree crown high point group area | region C1, C2, C3, ... displayed on the display. It is a figure which shows the tree crown high point group area | region according to the hierarchy of each hierarchy from the 1st hierarchy to the 5th hierarchy, and the tree vertex in the said tree hierarchy high dot group area concerned. Crown high point cloud data for all hierarchies obtained by sequentially performing the crown high point cloud data creation processing (step S53) and tree vertex calculation processing (step S54) by hierarchy from the first hierarchy (top layer) to the bottom layer It is a figure which shows an area | region and a tree vertex. It is a figure which shows the precision tree crown image displayed on the display. It is a figure which shows the list of single tree resource information displayed on the display. It is a figure which shows the list of the forest resource summary information displayed on the display. It is a figure shown in order to demonstrate the forest resource information calculation apparatus 1 which concerns on embodiment. FIG. 15 is a diagram illustrating a configuration of a hierarchical tree vertex extraction unit 50 illustrated in FIG. 14.

  FIG. 1 is a flowchart for explaining the forest resource information calculation method according to the embodiment. The forest resource information calculation method according to the embodiment is a forest resource information calculation method for creating forest resource information related to a survey target forest area based on image data corresponding to an area including the survey target forest area. Here, the image data corresponding to the area including the forest area to be surveyed is acquired by taking an aerial image of the area including the forest area to be surveyed from above. In addition, although an aircraft, a drone, etc. can be used for aerial photography, in the forest resource information calculation method according to the embodiment, a popular drone is used.

  In the forest resource information calculation method according to the embodiment, the survey target tree refers to a planted tree that is a high tree such as larch, red pine, cedar, cypress, etc. It is assumed that the forest formed by the trees is a forest area having a predetermined area. In addition, when the “survey target tree” is described as a single tree, it may be simply abbreviated as “each tree”. The same applies to a forest resource information calculation apparatus according to an embodiment described later.

  As shown in FIG. 1, the processing performed in the forest resource information calculation method according to the embodiment includes data input processing (step S10) for inputting aerial image data captured by a drone to an information processing apparatus such as a personal computer, A 3D point cloud data creation process (step S20) for creating 3D point cloud data based on the input aerial image data and a 3D point cloud created by the 3D point cloud data creation process (step S20). Survey target forest area image data creation processing (step S30) for creating survey target forest area image data based on the data and geographical information obtained from the geographic information system (GIS) 100, and survey target forest area Based on the survey target forest area image data created by the image data creation process (step S30), the survey target forest It was created by a crown height image data creation process (step S40) for creating crown height image data representing the crown height of each tree existing in the area as a crown height, and a crown height image data creation process (step S40). Among the crown heights obtained from the crown height image data, the hierarchy having the maximum crown height is defined as the first hierarchy (top layer), and a plurality of hierarchies having a hierarchy width of a predetermined length are set from the first hierarchy toward the ground surface. And a tree-specific tree vertex extraction process (step S50) for extracting, for each hierarchy, tree vertices of a tree crown having tree vertices in each hierarchy including the first hierarchy.

In addition to these steps S10 to S50, precise crown image data creation processing (step S60) for creating precise crown image data in which individual crowns are divided, and precise crown image data creation processing (step S60). From the precise crown image data created by the above, it corresponds to the precise crown information creation process (step S70) for creating precise crown information corresponding to each tree, and to each tree created by the precise crown information creation process (step S70). Forest resource information calculation processing (step S80) for creating forest resource information related to the forest area to be investigated based on the precise tree crown information to be performed.
The precise crown image data creation processing is based on the crown vertices extracted for each hierarchy by the hierarchical tree vertex extraction process (step S50), and the adjacent crowns from the crown height of the crown. Using the area dividing algorithm for dividing the area, precise crown image data in which individual crowns are divided is created.

  In addition, the forest resource information calculation process (step S80) includes a breast height diameter calculation process (step S81) for calculating a breast height diameter of each tree as a breast height diameter in each tree, and the volume of each tree. In the material integration determination process (step S82) to be calculated, the chest height diameter of each tree, the volume of each tree, the precise crown information created by the precise crown information creation process (step S70), and the tree vertex extraction process by hierarchy (step S50) Forest resource information totaling processing (step S83) for performing processing for totalizing the height of each tree obtained based on the extracted tree vertices is included.

  The tree-specific tree vertex extraction process (step S50) includes a plurality of processes, and each of these processes will be described in detail with reference to FIG.

  Subsequently, each process after the three-dimensional point cloud data creation process (step S20) among the above-described steps S10 to S80 will be described in detail. The three-dimensional point cloud data creation process (step S20) is a camera in which a predetermined area including the investigation target area is mounted on the drone. From the data (aerial image data), the SFM (Structure from Motion), which is a well-known three-dimensional shape restoration technique, creates an ortho-corrected map image without distortion, and the three-dimensional point cloud data from the created ortho image Create

  The area of the entire predetermined area including the forest area to be surveyed is about 2 to 3 hectares. With a camera mounted on the drone, including the forest area to be surveyed having such an area, a few hundreds of images are taken while overlapping and shifting about 80%, and from the captured images, SFM To create orthorectified map images without distortion. Based on the ortho image created in this way, three-dimensional point cloud data representing the height can be created. In addition, about SFM, it describes in the following well-known literature 1.

  Known Document 1: Linda G. Shapiro; George C. Stockman (2001). Computer Vision. Prentice Hall.

  The survey target forest area image data creation process (step S30) is the geographical information obtained from the geographic information system (GIS) 100 (in this case, the forest registered in the forest management ledger etc. maintained in the prefectures, etc.) Area boundary data), image data of the forest area to be investigated (survey area) is extracted from the three-dimensional point cloud data to obtain the forest area image data to be investigated. Then, the survey target forest area image corresponding to the survey target forest area image data is displayed on the display.

  FIG. 2 is a diagram illustrating a survey target forest area image displayed on the display. FIG. 2 shows an extracted part of the forest area to be surveyed. According to the forest area image to be surveyed shown in FIG. 2, the crown of each tree can be identified. Since FIG. 2 is a monochrome image, it is difficult to identify the crown of each tree. However, the crown of each tree can be easily identified on the original color image of FIG.

  Thereafter, based on the survey target forest area image data, a canopy height image data creation process (step S40) is performed to create crown height image data representing the crown height of a tree existing in the survey target forest area. The crown height image data creation process (step S40) is based on the survey target forest area image data created by the survey target forest area image data creation process (step S20), and the crown height of a tree existing in the survey target forest area. The crown height image data representing (the height of the crown from the ground surface) is created.

  Specifically, based on the forest area image data to be surveyed, meshed digital surface model data (data representing surface elevations such as trees) and digital elevation model data (data representing ground surface elevations) are created. Then, by obtaining the difference between the digital surface model data and the digital elevation model data in each mesh, the crown height image data in which the tree height is represented in each mesh is created. A canopy height image corresponding to the canopy height image data created in this way is displayed on the display.

  The mesh size can be set based on the ground resolution of the forest area image to be surveyed. However, if the mesh size is too large, there may be a problem in the accuracy of the calculated forest resource information. Conversely, if the mesh size is too small, the amount of data to be processed increases. For this reason, the mesh size is preferably set to an optimum size in consideration of the area of the forest area to be surveyed, the state of the forest in the forest area to be surveyed, the accuracy of the calculated forest resource information, and the like. In the embodiment, the mesh size is 10 cm × 10 cm.

  FIG. 3 is a crown height image displayed on the display. Note that the gradation from black to white in the crown height image shown in FIG. 3 changes in height with respect to the reference when the ground surface is used as the reference (0.0 meters) (for example, 0.0 meters is black). Represents. It should be noted that the crown height image data corresponding to the crown height image shown in FIG. 3 is not clear at this stage.

  Subsequently, tree-specific tree vertex extraction processing (step S50) is performed based on the crown height image data created by the crown height image data creation processing (step S40).

  FIG. 4 is a flowchart for explaining in detail the hierarchical tree vertex extraction process (step S50) shown in FIG. As shown in FIG. 4, the tree-specific tree vertex extraction process (step S50) includes an averaging process (step S51), an initial value setting process (step S52), and a tree-based tree crown high point group data generation process (step S53). Tree vertex calculation processing (step S54) is included. In addition to these processes, calculation processes such as Hmax−D × Ni = Hi (step S55), Hi> 0 (step S56), and Ni = Ni + 1 (step S57) are included. Hmax is the maximum crown height in the forest area to be investigated, D is the hierarchy width, Ni is the current processing target hierarchy, and Hi is the maximum crown term in the next processing target hierarchy, which will be described later.

  The averaging process (step S51) is performed when, for example, the tree crown height image shown in FIG. 3 includes extreme irregularities (such as a portion where branches and leaves are sparse compared to the surroundings, or a chief branch leaf). This is a process for removing these as noise and creating canopy high image data from which the noise has been removed. In the averaging process (step S51), each mesh of the crown height image data created by the canopy height image data creation process (step S40) is set as a processing target mesh, and an averaging filter is superimposed on the processing target mesh. The average value of the crown height of each mesh included in the averaging filter is set as the crown height of the mesh to be processed. Such processing is sequentially performed on each mesh to be processed, thereby generating tree height image data from which noise has been removed.

  The averaging process will be described in a simplified manner. When the size of one mesh is assumed to be 10 cm on one side, an averaging filter (filter size is 5 mesh × for example) so that one mesh to be averaged (referred to as a processing target mesh) is the center. 5 meshes) are overlapped, and the processing of setting the average crown height of each mesh included in the averaging filter to the crown height of the processing target mesh is sequentially performed on each processing target mesh.

  For example, the crown heights of the respective meshes (for example, a total of 25 meshes of 5 mesh × 5 mesh) included in the range superimposed on the averaging filter (referred to as the averaging processing range) are integrated, A value obtained by dividing the total value by 25 is set as an average value of the averaging processing range, and the average value is set as a crown height of the processing target mesh. Such processing is performed on all meshes included in the crown height image data. The size of the averaging filter can be set based on the average crown diameter of the survey target tree existing in the survey target forest area.

  In addition, when such an averaging process is performed, the crown height for each mesh obtained by the averaging process may differ from the original crown height (the crown height for each mesh). If the size is as small as 10 cm on a side, the crown height for each mesh obtained by the averaging process can obtain a crown height that is not significantly different from the original crown height. In addition, by performing such an averaging process, the extreme unevenness (such as the part where branches and leaves are sparse compared to the surroundings and lengthy branches and leaves) existing in each canopy is removed, and each canopy is smoothed. Therefore, when performing region division by the Watershed algorithm, which will be described later, highly accurate region division can be performed.

  The initial value setting process (step S52) is a process of setting an initial value when performing the tree-based crown high point cloud data creation process (step S53), and the initial value here is an averaging process (step S51). ) The maximum crown height (maximum crown height in the forest area to be surveyed) Hmax of the canopy height for each mesh obtained by the above and the maximum crown height Hmax as a starting point and the hierarchy is set downward (ground) And the current processing target hierarchy Ni can be exemplified.

  The maximum crown height Hmax among the crown heights of each mesh is, for example, the crown height obtained in a mesh among the crown heights obtained in each mesh is 25 m, and the crown height of the 25 m is If it is the maximum crown height among the crown heights obtained in all meshes, Hmax = 25 m. Further, the layer width D is set to D = 1 when the layer width is set, for example, every 1 m from the maximum crown height Hmax to the downward direction (ground). Further, the processing target hierarchy Ni is initially set to Ni = 1 because the hierarchy to be processed in the initial stage is the first hierarchy (the uppermost layer).

  As described above, Hmax = 25, D = 1, and Ni = 1 are set as initial values, and the tree-specific crown high point group data creation process (step S53) is performed. In this case, since Ni = 1, the tree-specific crown high point group data creation process in the first layer (top layer) is performed. Since Hmax = 25 m and D = 1, the first hierarchy is 25 m to 24 m.

  That is, in the tree-specific crown high point cloud data creation process (step S53), the layer in which the maximum crown height (Hmax = 25 m) of the crown heights of each mesh calculated by the averaging process exists is defined as the first hierarchy (the highest level). As the upper layer), a hierarchy width (D = 1m) of a predetermined length is set starting from the maximum crown height (Hmax), and the hierarchy is present in each hierarchy including the first hierarchy for each hierarchy width from the maximum crown height Hmax = 25m. Hierarchical crown high point group data corresponding to individual tree crowns is sequentially created for each hierarchy.

The tree-specific crown high point cloud data creation process (step S53) will be specifically described. In this case, the maximum crown height Hmax among the crown heights of each mesh is 25 m, and the layer width is 1 m. Therefore, Hmax = 25 and Ni = 1 (first layer) are set as initial values. . In this case, first, crown high point group data (referred to as first layer crown high point group data) corresponding to the crown belonging to the first hierarchy (24 m to 25 m) of the crown height of each mesh is created. The first level is 24 m to 25 m, but strictly speaking, the value is slightly higher than 24 m to 25 m, such as 24.01 m to 25 m. This is the same in the lower hierarchy than the first hierarchy. For example, when the second hierarchy is set to 23 m to 24 m, from 23.01 m to 24 m, a value slightly exceeding 23 m to 24 m. To do.
Then, the first hierarchical crown high point cloud region corresponding to the first hierarchical crown high point cloud data created by the hierarchical crown high point cloud data creation processing (step S53) is displayed on the display.

  Here, the first hierarchy is set to 24.01 m to 25 m, and the second hierarchy is set to 23.01 m to 24 m. However, the setting is not limited to this, and for example, the first hierarchy is 24 m to 24 m. .99m, the second layer may be set as 23m to 23.99m. The same applies to other layers. However, regarding the first layer, the first layer is the uppermost layer, and there is no layer above it, so it may be 24 m to 25 m.

  FIG. 5 is a diagram showing the first-level tree crown high point group region displayed on the display. In FIG. 5, the areas painted in black are the first hierarchical tree crown high point group areas A1, A2, A3,..., And the first hierarchical tree crown high point group areas A1, A2, A3,. Indicates the presence of a tree in which the vicinity of the tree top reaches the first hierarchy (24 m to 25 m). For example, in FIG. 5, when looking at the state of a group of trees contained in the black frame (cross-sectional view), in these tree groups, the vicinity of the top of the tree reaches the first level shown in a band shape. It can be seen that there are trees.

  In FIG. 5, there are actually a large number of first hierarchical crown high point group areas A1, A2, A3,... Has been. The same applies to the canopy high point cloud region extracted in another hierarchy described later.

  In addition, since FIG. 5 is a monochrome image, in FIG. 5, the first hierarchy and the first hierarchy crown high point group areas A1, A2, A3,. However, in order to clarify the distinction from the respective layers obtained in the second layer and below described later, the first layer and the crown high point group shown in a band shape on the original color image of FIG. The regions A1, A2, A3,... Are shown in red.

  Thus, when the first hierarchical crown high point group areas A1, A2, A3,... Are extracted, the first hierarchical crown high point group areas A1, A2, A3,. Using the local maximum value filter (for example, 3 mesh × 3 mesh), the maximum value in each of the first hierarchical tree crown high point group areas A1, A2, A3,. A tree vertex calculation process (step S54) is performed with the tree vertex of each tree crown. In addition, since the tree top of the tree is the tree top of the tree having the tree crown, the tree height of the tree having the tree crown can be obtained by obtaining the tree vertex of the tree crown.

  FIG. 6 is a diagram showing tree vertices a1, a2, a3,... Extracted in the first hierarchical tree crown high point group regions A1, A2, A3,. Note that the current tree vertex obtained in the current processing target hierarchy (in this case, the first hierarchy) is indicated by a black dot.

  In this way, when the processing for extracting the tree vertices a1, a2, a3,... In the first hierarchy crown high point group areas A1, A2, A3,. A process (step S55) for calculating a tree crown height−D (hierarchy width) × Ni (current process target hierarchy) = Hi (maximum tree crown height in the next process target hierarchy) is performed.

  The maximum crown height Hi in the next processing target hierarchy is the maximum crown height (Hmax) in the survey target forest area = 25 m and the hierarchical width (D) = 1 m, and the processing target hierarchy Ni = Since it is 1, 25 m-1 m × 1 = 24 m, and the maximum crown height Hi in the next processing target hierarchy is 24 m. Then, it is determined whether or not Hi> 0 (step S). In this case, since Hi = 24 and Hi> 0, Ni = Ni + 1 is performed (step S57), and Ni = 2 is set as the processing target. The hierarchy is set to the second hierarchy, and the process returns to the tree-specific crown high point cloud data creation process (step S53).

  Here, the second hierarchy is a hierarchy having a crown height of 23 m to 24 m, and the hierarchical crown-high-point group data creation process (step S53) is performed in the second hierarchy. That is, among the tree crown heights of each mesh, tree crown high point group data belonging to the second layer (referred to as second layer tree crown high point group data) is created. And the 2nd hierarchy crown high point group area | region corresponding to this 2nd hierarchy crown high point cloud data is displayed on a display.

  FIG. 7 is a diagram showing second-level tree crown high point group regions B1, B2, B3,... Displayed on the display. In addition, in FIG. 7, it extracted in the said 2nd hierarchy crown high point group area | region B1, B2, B3, ... not only in 2nd hierarchy crown high point group area | region B1, B2, B3 .... The tree vertices are also shown. In FIG. 7, the areas painted in gray are the second hierarchical crown high point group areas B1, B2, B3,..., And the second hierarchical crown high point group areas B1, B2, B3,. .. indicates the presence of a tree in which the vicinity of the tree top reaches the second hierarchy (23 m to 24 m). For example, in FIG. 7, when looking at the state of the group of trees contained in the black frame (cross-sectional view), in these groups of trees, the vicinity of the top of the tree reaches the second level shown in a band shape. It can be seen that there are trees.

  Since FIG. 7 is a monochrome image, in FIG. 7, the second hierarchy and the second hierarchy crown high point group regions B1, B2, B3,. However, in order to clarify the distinction from the crown high point group area of each layer obtained in other layers, the second layer and the second layer shown in the band shape on the original color image of FIG. The two-level tree crown high point group regions B1, B2, B3,... Are shown in orange.

  In FIG. 7, it is easy to distinguish the newly extracted second-level tree crown high point group region from the already extracted first-level tree crown high point group regions A1, A2, A3,. In order to do so, the first-level tree crown high point group areas A1, A2, A3,... Already extracted are shown in white.

  As shown in FIG. 7, among the second hierarchy crown high point group areas B1, B2, B3,..., The already extracted crown high point group area (in this case, the first hierarchy crown high point group area A1). , A2, A3,...) Extracted from the same tree crown as the second hierarchical crown high point group area (for example, the second hierarchical crown high point group area B4, B5, etc.) It is displayed as a canopy high point cloud region that forms a ring surrounding the first hierarchical crown high point cloud region A1, A2. Here, the crown high point cloud region extracted in the same crown as the already extracted crown high point cloud region is defined as the “extracted crown high point cloud region”, and a new one other than the “extracted crown high point cloud region”. The tree crown high point cloud region extracted in the above is defined as a “new tree crown high point cloud region”.

  Thus, when the second hierarchical tree crown high point group regions B1, B2, B3,... Are extracted, the second hierarchical tree crown high point group regions B1, B2, B3,. Using the local maximum value filter (for example, 3 mesh × 3 mesh), the maximum value in each of the second hierarchical tree crown high point group regions B1, B2, B3,. A tree vertex calculation process (step S54) is performed with the tree vertex of each tree crown.

  At this time, among the crown high point cloud data classified by hierarchy corresponding to individual crowns created in the hierarchy where the maximum value is to be calculated (in this case, the second hierarchy), the crown high point cloud data classified by hierarchy has already been created. The maximum value is calculated by excluding the tree-based crown high point cloud data created corresponding to the same crown as the existing crown. Referring to FIG. 7 as an example, among the second hierarchy crown high point group regions, for example, the crown high point group regions B4, B5, etc. Point cloud regions B4, B5, etc. are not subject to tree vertex calculation.

  In this way, the tree vertices b1, b2, b3,... Extracted in the second hierarchical tree crown high point group regions B1, B2, B3,... Are indicated by black dots in FIG. Since FIG. 7 is a monochrome image, in FIG. 7, tree vertices b1, b2, b3,... Extracted in the second hierarchical tree crown high point group regions B1, B2, B3,. Each of the tree vertices in the extracted first-tier tree crown high point group region is indicated by a black dot, but in order to clarify the distinction between the two, it has already been extracted on the original color image in FIG. The tree vertices in the crown high point group region of the first layer (in this case, the first layer) are indicated by red dots.

  When the processing of extracting the tree vertices b1, b2, b3,... In the second hierarchy crown high point group area is finished, Hmax (maximum crown height in the forest area to be investigated) −D (hierarchy width) × Ni (current Processing for obtaining (processing target hierarchy) = Hi (maximum crown height in the next processing target hierarchy) is performed (step S55). The maximum crown height Hi in the next processing target hierarchy is the maximum crown height (Hmax) in the survey target forest area = 25 m and the hierarchical width (D) = 1 m, and the processing target hierarchy Ni = Since it is 2, 25 m-1 m × 2 = 23 m, and the maximum crown height Hi in the next processing target hierarchy is 23 m. Then, it is determined whether or not Hi> 0 (step S). In this case, since Hi = 23 and Hi> 0, Ni = Ni + 1 = 2 + 1 is performed (step S57), and Ni = 3. The processing target hierarchy is set to the second hierarchy, and the process returns to the tree-specific crown high point cloud data creation process S53.

  Here, the third hierarchy is a hierarchy having a crown height of 22 m to 23 m, and the hierarchical crown-high-point group data creation process (step S53) is performed in the third hierarchy. That is, among the tree crown heights of each mesh, tree crown high point group data belonging to the third layer (referred to as third layer tree crown high point group data) is created. And the 3rd hierarchy crown high point group area | region corresponding to this 3rd hierarchy crown high point cloud data is displayed on a display.

  FIG. 8 is a diagram showing third-layer tree crown high point group regions C1, C2, C3,... Displayed on the display. In addition, in FIG. 8, it extracted in not only 3rd hierarchy crown high point group area | region C1, C2, C3 ... but the said 3rd hierarchy crown high point group area | region C1, C2, C3, .... The tree vertices are also shown. In FIG. 8, the grayed out areas are the third hierarchy crown high point group areas C1, C2, C3,..., And the third hierarchy crown high point group areas C1, C2, C3,. .. indicates the presence of a tree whose top of the tree has reached the third level (22 m to 23 m). For example, in FIG. 8, when looking at the state of the group of trees included in the black frame (cross-sectional view), in these tree groups, the vicinity of the top of the tree reaches the third level shown in a band shape. It can be seen that there are trees.

  Since FIG. 8 is a monochrome image, in FIG. 8, the third hierarchy and the third hierarchy crown high point group areas C1, C2, C3,. However, in order to clarify the distinction from the crown high point group area of each hierarchy obtained in other hierarchies, the third hierarchy and the second hierarchy shown in the band shape on the original color image of FIG. Three-level tree crown high point group regions C1, C2, C3,... Are shown in yellow.

  In FIG. 8, the newly extracted second-level tree crown high point group region, the first-level tree crown high-point group regions A1, A2, A3,. Point cloud region B1. In order to make it easy to distinguish from B2, B3,..., The first-level tree crown high-point group areas A1, A2, A3,. B2, B3,... Are shown in white.

  In this way, when the third hierarchy crown high point group areas C1, C2, C3,... Are extracted, the third hierarchy crown high point group areas C1, C2, C3,. Using a local maximum value filter (for example, 3 mesh × 3 mesh), the maximum value in each of the third-layer tree crown high point group regions C1, C2, C3,. A tree vertex calculation process (step S54) is performed with the tree vertex as the tree vertex.

  Also in this case, among the tree-based crown high point group data corresponding to the individual tree crowns created in the hierarchy where the maximum value is to be calculated (in this case, the third hierarchy), the tree-based crown high point group data has already been created. The maximum value is calculated by excluding the hierarchical crown point data corresponding to the same crown as the existing crown. Referring to FIG. 8 as an example, among the third-layer crown high point group regions, for example, the crown high point group regions C4 and C5 are assumed to be already extracted crown high point group regions, and the already extracted tree crowns are assumed. The high point cloud regions C4 and C5 are not subject to tree vertex calculation.

  In this way, the tree vertices c1, c2, c3,... Extracted in the third hierarchical tree crown high point group regions B1, B2, B3,... Are indicated by black dots in FIG. . Since FIG. 8 is a monochrome image, in FIG. 8, each of the tree vertices extracted in the canopy high point cloud region of all the layers so far is indicated by a black dot, but has already been extracted. In order to clarify the distinction between the tree vertices in the first hierarchy crown high point group area and the tree vertices extracted in the second hierarchy and the first hierarchy crown high point cloud area, the original color image on FIG. The tree vertices extracted in the second hierarchy and the first hierarchy crown high point group regions are indicated by red dots.

  When the process of extracting the tree vertices c1, c2, c3,... In the third hierarchy crown high point group area is completed as described above, the same processes as described above (steps S55, S56, S57 are performed to Separate crown high point group data creation processing (step S53) and tree vertex calculation processing (step S54) are sequentially repeated, such processing may be performed until Hi = 0, but Hi = It does not need to be performed until it reaches 0, and can be set as appropriate according to the condition of the forest area to be surveyed. For example, even if it ends with Hi = 5 (the maximum crown height in the next processing hierarchy is about 5 m) Good.

  FIG. 9 is a diagram illustrating a tree-based crown high point group region of each layer from the first layer to the fifth layer and tree vertices in the tree-specific tree crown high point group region. In FIG. 9, the tree-based crown high point group areas (first to fifth hierarchy crown high-point group areas) extracted in each hierarchy are shown in different colors for each hierarchy. Since FIG. 9 is a monochrome image, it is difficult to read each layer by color. However, in practice, the first layer is red and the second layer is the original color image in FIG. Orange, the third hierarchy is yellow, the fourth hierarchy is yellow-green, and the fifth hierarchy is green. And the maximum value (tree vertex) in the new crown high point group area | region extracted in each hierarchy is shown by the point of a black circle. By sequentially performing such processing up to a lower hierarchy, it is possible to obtain a crown high point cloud image of each tree for each hierarchy.

  FIG. 10 shows the hierarchal tree high point cloud data creation process (step S53) and the tree apex calculation process (step S54) for all hierarchies obtained by sequentially performing the first hierarchy (top layer) to the bottom layer. It is a figure which shows a tree crown high point group area | region and a tree vertex. By performing the tree crown high point cloud data creation process (step S53) and tree vertex calculation process (step S54) by hierarchy from the first hierarchy (top layer) to the bottom layer in sequence, the first hierarchy (top layer) ) To the lowest layer, it is possible to extract the crown vertices of each level and the tree vertices of all the crowns existing in the forest area to be surveyed. In FIG. 10, the black circle points are the tree vertices of individual crowns for each hierarchy. Here, since the tree vertices of each tree crown are the tree vertices of each tree having the tree crown, the tree height of the tree having the tree crown can be obtained by obtaining the tree vertex of the tree crown. The tree height of each tree can be obtained from noise-removed tree crown height image data from which noise has been removed by the averaging process (step S51).

  In FIG. 10, the tree-based crown high point group regions extracted in each hierarchy are shown in different colors for each hierarchy. Note that since FIG. 10 is a monochrome image, it is difficult to read each layer by color, but in reality, the first layer is red and the second layer is on the original color image of FIG. Orange, the third hierarchy is yellow, the fourth hierarchy is yellow-green, and the fifth hierarchy is green.

  In this way, when all tree vertices existing in the forest area to be investigated have been extracted, the precise canopy image data creation process (step S60) in the flowchart of FIG. 1 is performed. This precise canopy image data creation process is a process for creating precise canopy image data that can extract the canopy corresponding to each tree with high accuracy.

  Based on the crown vertices extracted for each hierarchy, the precise canopy image data creation process uses the area division algorithm that divides the adjacent crowns into areas based on the crown height of the crowns. Precise canopy image data in which the canopy is divided is created. Specifically, using the tree vertices of the extracted crowns for each hierarchy as seeds, the z values (crown height image data obtained by the canopy height image data creation process in step S40) are used to obtain known areas. A precise tree image is created using the Watershed algorithm which is a division method. In the forest resource information calculation method according to the embodiment, as the z value, noise-removed canopy height image data from which noise has been removed by the averaging process (step S51) shown in FIG. 4 is used.

  As described above, the crown height image data from which noise has been removed is removed from the individual crowns by removing the extreme irregularities (such as the sparse branches and lengthy branches and leaves) from the individual crowns. Is smoothed. For this reason, when performing the region division by the Watershed algorithm, the region division by the Watershed algorithm can be performed with high accuracy by using such noise-removed canopy height image data.

  The Watershed algorithm is an algorithm that can extract the boundaries of individual objects with high accuracy and classify the individual objects when a large number of adjacent dense objects exist in a connected state. The Watershed algorithm is described in publicly known document 2 shown below.

  Known Document 2: Salman N. and Liu C. Q., “Image Segmentation and Edge Detection Based on Watershed Techniques,” International Journal of Computers and Applications, vol. 25, no. 4, pp. 258-263, 2003

  The precise canopy image data creation process (step S60) creates precise canopy image data using the Watershed algorithm, and the precise canopy image corresponding to the precise canopy image data created by the precise canopy image data creation process (step S60). Is displayed on the display.

  FIG. 11 is a diagram showing a precise tree crown image displayed on the display. As shown in FIG. 11, the precise crown image created using the Watershed algorithm is obtained by dividing the individual crowns into regions so as to surround the tree vertices of the individual trees. In FIG. 11, the area surrounded by the frame line L is an individual tree crown.

  In FIG. 11, the tree-based crown high point group regions extracted in each hierarchy are shown in different colors for each hierarchy. Since FIG. 11 is a monochrome image, it is difficult to read each layer by color, but in reality, the first layer is similar to FIG. 10 on the original color image of FIG. Red, the second layer is orange, the third layer is yellow, the fourth layer is yellow-green, and the fifth layer is green.

  As can be seen from the precise crown image shown in FIG. 11, the crowns corresponding to the trees obtained by the region division by the Watershed algorithm are represented with high accuracy for each hierarchy. It will be. As mentioned above, the Watershed algorithm is an algorithm that can extract the boundaries of individual objects with high accuracy when a large number of adjacent dense objects are connected to each other. It is. Therefore, by using the Watershed algorithm, a densely existing canopy can be classified with high accuracy as individual canopies regardless of the size of the canopy (crown diameter or height).

  In this way, when the precise canopy image data is created, a precise canopy information creation process (step S70) is performed based on the precise canopy image data to obtain precise information on the canopy of each tree (precise canopy information). ). As the precise crown information created in the precise crown information creation process (step S70), at least the label number (serial number) of the crown corresponding to each tree, the crown position (center position of the crown) of the crown corresponding to each tree, The crown diameter of the crown corresponding to each tree, the crown area of the crown corresponding to each tree, etc. can be exemplified. Besides this, the crown shape of the crown corresponding to each tree can also be obtained as precise crown information. .

  Subsequently, forest resource information calculation processing (step S80) is performed. In the forest resource information calculation process, information on forest resources is calculated based on the precise tree crown information created in the precise tree crown information creation process (step S70). Examples of information relating to forest resources calculated in the forest resource information calculation process (step S80) include the breast height diameter of each tree and the volume of each tree. In addition, as information regarding forest resources, the tree height of each tree can be cited in addition to the chest height diameter of each tree and the volume of each tree, and the tree height is determined by tree-level tree vertex extraction processing (step S50). Based on the tree vertices calculated in the tree vertex calculation process (step S54), the tree height of each tree can be obtained.

  In order to calculate the information regarding these forest resources, in the forest resource information calculation process (step S80), the breast height diameter calculation process (step S81) for calculating the breast height diameter of each tree and the volume as the resource of each tree are calculated. The material integration determination process (step S82) to be performed and the forest resource information totaling process (step S83) for totaling the forest resource information are performed.

  In the breast height diameter calculation process (step S81), the breast height diameter is calculated by a multiple regression equation from at least one of the crown area and the crown diameter having a strong correlation with the breast height diameter and the tree height. In addition, the breast height diameter of unmeasured trees can be calculated by interpolation. This makes it possible to calculate the breast height diameter of all the survey target trees existing in the survey target forest area. In addition, when calculating multiple regression equations, it is possible to determine multiple regression equation variables by selecting about ten standard trees in the field survey for each tree type and measuring the crown area, crown diameter and tree height of the selected standard tree. preferable.

  In the material integration determination process (step S82), the material volume is obtained from a two-variable material volume expression of the breast height diameter and the tree height. It should be noted that a quick reference table in which the volume is calculated from the breast height diameter and the tree height is known, and the tree resource (the volume) can be determined from such a quick reference table. The quick reference table is described in the following documents (known document 3 and known document 4).

Known Document 3: Forestry Agency Planning Division, Tachiki Trunk Volume Table East Japan, Japan Forestry Research Committee, 334 pages, 2003

  The forest resource information totaling process (step S83) totals the resource information of each tree such as the number of trees to be investigated, the crown diameter, the crown area, the breast height diameter, the tree height, and the volume of each tree, that is, single tree resource information. . Then, the aggregated single tree resource information is registered in the attribute database. Single tree resource information registered in the attribute database can be displayed on the display as a list. The single tree resource information displayed as a list includes XY coordinates that represent the center position of the tree crown. The number of survey target trees can be obtained based on the label number obtained by the precise canopy information creation process (step S70).

  By the way, the above-mentioned “crown diameter” represents the size of the horizontal expansion of the canopy when viewed in plan, and the “chest height diameter” is the thickness of the trunk of the tree (in each tree). It represents the diameter of the human breast. Note that the crown and trunk of the tree are not actually a perfect circle, so it cannot be strictly called a “diameter”. However, in this specification, the term “crown” is used to indicate the size of the horizontal spread of the crown. “Diameter”, and “Chest height diameter” as the thickness of the trunk.

  FIG. 12 is a diagram showing a list of single tree resource information displayed on the display. As shown in FIG. 12, as the single-tree resource information of the forest trees to be surveyed, the crown number (label number) corresponding to each tree, the XY coordinates representing the center position of each crown, the crown diameter, the crown area, the chest height The diameter (indicated as “DBH” in FIG. 12), tree height, and volume are displayed in association with the number (label number) of the tree crown.

  In addition, the forest resource information aggregation processing (step S83) includes the number of survey target trees, the average value of the crown diameter calculated for each tree, the maximum value of the crown diameters calculated for each tree, and Minimum value, average value of crown area calculated for each tree, maximum and minimum values of crown area calculated for each tree, average value of chest height diameter calculated for each tree, Average breast height diameter calculated for each tree, maximum and minimum breast height diameters calculated for each tree, average tree height calculated for each tree, for each tree The maximum and minimum values of the calculated tree height, the average value of the volume calculated for each tree, the maximum and minimum values of the volume calculated for each tree, The total volume is also calculated and the forest volume Calculate the amount of forest resources per hectare (ha) used in the management (here, the number of trees and the volume of wood), and the information indicating the summary of the forest resources based on these results (forest resource summary information) Is also registered in the attribute database. The forest resource summary information registered in the attribute database can be displayed on the display as a list.

  FIG. 13 is a diagram showing a list of forest resource summary information displayed on the display. As shown in FIG. 13, the number of trees to be investigated, the average value of the crown diameter, the maximum and minimum values of the crown diameter, the average value of the crown area, the maximum and minimum values of the crown area, the average value of the breast height diameter, the breast height The average diameter, maximum and minimum breast height diameter, average tree height, maximum and minimum tree height, average volume, maximum and minimum volume, and total volume are displayed. The amount of forest resources per hectare (ha) used in forest management (here, the number of trees and the volume of wood) is displayed. By displaying such forest resource summary information on the display, it is possible to grasp the summary of forest resources in the forest area to be investigated.

  In this specification, the single tree resource information shown in FIG. 12 and the forest resource summary information shown in FIG. 13 are combined as forest resource information. By combining such forest resource information with existing forest survey database (for example, database of forest management ledger etc. maintained in prefectures, etc.), it is possible to grasp forest conditions and plan forest operations such as thinning. Can be used for forest management. For example, when trying to thin a forest, it is useful when making a simulation of a thinning plan.

  As described above, in the forest resource information calculation method according to the embodiment, three-dimensional point cloud data is created from aerial image data obtained by photographing a region including a forest area to be surveyed by a drone or the like. Based on the group data and geographical information, survey target forest area image data is created, and based on the survey target forest area image data, the crown height image data representing the crown height of trees existing in the survey target forest area Create Then, after generating noise-removed canopy height image data obtained by removing noise from the canopy height image data by averaging processing, the noise is present in each hierarchy including the first hierarchy based on the noise-removed canopy height image data. The tree-specific crown high-point cloud data corresponding to each canopy is created for each hierarchy, and the tree-based crown high-point cloud data corresponding to each tree canopy created for each hierarchy is used to create a tree-based crown corresponding to each tree crown. The maximum value of high point cloud data is calculated.

  Here, the crown high point cloud data for each hierarchy is created for each hierarchy, and the crown high point cloud for each hierarchy corresponding to each canopy from the crown high point cloud data for each hierarchy created for each hierarchy. The process of calculating the maximum value of the data is, for example, by creating individual tree crown high point cloud data by dividing the individual tree canopy into layers like CT (Computed Tomography) scan, This is a process of calculating the maximum value from the obtained tree-based crown high point cloud data. By performing such processing, the maximum value of each tree crown existing in each hierarchy can be calculated for each hierarchy. Note that the “tree apex of the tree crown” calculated for each hierarchy is the tree apex of the tree having the tree crown, and the tree height of the tree can be obtained from the calculated tree apex.

  In this way, after calculating the maximum value (tree apex) of each individual crown in each hierarchy for each hierarchy, each crown based on the tree apex of the tree extracted for each hierarchy (seed) From the height of the canopy, using a region dividing algorithm (watershed algorithm), precise crown image data in which adjacent canopies are divided into individual canopies is created.

  The precise canopy image data created in this way becomes precise canopy image data from which individual canopies corresponding to the trees to be investigated can be extracted with high accuracy. Therefore, the precise canopy information created based on the precise canopy image data becomes highly accurate canopy information corresponding to each tree of the survey target tree, and based on the precise canopy information, information on forest resources (forest resource information ) To obtain highly accurate forest resource information. As a result, it is possible to provide highly accurate forest resource information with little measurement error and high objectivity in the entire forest area to be surveyed, for each subgroup, and in any range, so it can be used for forest management such as forest management planning such as thinning. .

  In the embodiment, since a general camera and a popular drone can be used as photographing equipment for obtaining aerial image data, a system for calculating forest resources is inexpensive and easy. Can be realized.

  In addition, because aerial image data obtained by photographing the forest area to be surveyed with a camera is used, the understory vegetation under the canopy (between the canopy and the ground) Etc.) is difficult to be captured as image data, and the noise processing for removing the lower vegetation as noise can be reduced.

  The forest resource information calculation method 1 according to the embodiment has been described above. Next, the forest resource information calculation device according to the embodiment will be described.

  FIG. 14 is a diagram for explaining the forest resource information calculation apparatus 1 according to the embodiment. The forest resource information calculation device 1 according to the embodiment is a device for performing the processing of each step shown in FIGS. 1 and 4, and has an aerial data input unit 10 having a function of performing the processing of step S10 in FIG. 1. 1, the three-dimensional point cloud data creation unit 20 having the function of performing the process of step S20 in FIG. 1, the survey target forest area image data creation unit 30 having the function of performing the process of step S30 in FIG. The crown height image data creation unit 40 having the function of performing the process of step S40, the tree-specific tree vertex extraction unit 50 having the function of performing the process of step S50 in FIG. 1, and the function of performing the process of step S60 in FIG. 1 has a precise crown image data creation unit 60, a precise crown information creation unit 70 having the function of performing the process of step S70 in FIG. And forest resources information calculating unit 80 having a processing function for performing the step S80, with a.

  Further, the hierarchical tree vertex extraction unit 50 includes components for performing a plurality of processes, which will be described later with reference to FIG. Further, the forest resource information calculation unit 80 includes a breast height diameter calculation unit 81 having a function of performing the process of step S81 in FIG. 1, a material integration determining unit 82 having a function of performing the process of step S82 in FIG. And a forest resource information totaling unit 83 having a function of performing the process of step S83.

  FIG. 15 is a diagram illustrating a configuration of the tree-specific tree vertex extraction unit 50 illustrated in FIG. 14. As shown in FIG. 15, the hierarchical tree vertex extraction unit 50 performs the averaging processing unit 51 for performing the averaging process in step S51 in FIG. 4 and the initial value setting process in step S52 in FIG. The initial value setting unit 52, the hierarchical crown high point cloud data creation unit 53 for performing the hierarchical crown high point cloud data creation process of step S53 in FIG. 4, and the tree vertex calculation process of step S54 in FIG. 4 includes a tree vertex calculation unit 54 and a calculation processing unit 58 that performs calculations such as Hmax−D × Ni = Hi in step S55, Hi> 0 in step S56, and Ni = Ni + 1 in step S57 in FIG. Yes.

  As described above, each component of the forest resource information calculation apparatus 1 according to the embodiment shown in FIGS. 14 and 15 is processed in each step of the forest resource information calculation method according to the above-described embodiment (see FIGS. 1 and 4). Therefore, the forest resource information calculation apparatus 1 according to the embodiment can obtain the same effects as those obtained by the forest resource information calculation method according to the above-described embodiment. In addition, the description about the content of the process of the forest resource information calculation apparatus 1 which concerns on embodiment is abbreviate | omitted.

  In addition, the forest resource information calculation apparatus 1 according to the embodiment has functions installed in the respective constituent elements (see FIGS. 14 and 5) included in the forest resource information calculation apparatus 1 installed as a computer program. By giving predetermined data to each component, the functions of each component are executed on the software of the computer.

  Such software includes a survey target forest area image data creation processing step for creating survey target forest area image data based on the three-dimensional point cloud data and geographical information corresponding to the area including the survey target forest area, Canopy height image data creation processing step for creating crown height image data that represents the height of the crown of each tree existing in the survey target forest area as the crown height based on the survey target forest area image data, and the crown height image data Among the tree crown heights obtained from the above, a hierarchy having a maximum tree crown height is defined as a first hierarchy, and a plurality of hierarchies having a hierarchy width of a predetermined length from the maximum tree crown height as a starting point are set from the first hierarchy toward the ground surface. And a tree-specific tree vertex extraction processing step for extracting tree vertices of tree crowns having tree vertices in each hierarchy including the first hierarchy for each of the hierarchies. It may be.

  The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present invention. For example, the following modifications are possible.

  (1) In the above embodiment, for example, the case where the investigation target tree is a planted tree such as larch, red pine, cedar, cypress, etc. is exemplified, but the investigation target tree is not limited to the above-described planted tree. For example, in the case where there is a forest in which beech and oak are grouped within a predetermined area, the beech and oak can be used as a survey target tree.

  (2) In the above-described embodiment, the aerial image data is obtained using the drone. However, the aerial image data is not limited to the drone. For example, the aerial image data may be obtained using an aircraft. .

  (3) In the above-described embodiment, a map-corrected ortho image without distortion is created from aerial image data by SFM (Structure from Motion), which is a 3D shape restoration technique, and 3D is created from the created ortho image. Although point cloud data is created, three-dimensional point cloud data may be obtained based on laser measurement data obtained by irradiating laser light from above.

  DESCRIPTION OF SYMBOLS 1 ... Forest resource information calculation apparatus, 10 ... Aerial photography data input part, 20 ... Three-dimensional point cloud creation part, 30 ... Investigation object forest area image data creation part, 40 ... Crown height Image data creation unit 50 ... Hierarchical tree vertex extraction unit 51 ... Average processing unit 52 ... Initial value setting unit 53 ... Hierarchical crown high point group data creation unit 54 ... Tree vertex calculation unit, 58... Arithmetic processing unit, 60... Precise tree image data creation unit, 70... Precision tree crown information creation unit, 80 .. forest resource information calculation unit, 81. Calculation unit, 82... Material accumulation determination unit, 83... Forest resource information totaling unit, S 10 .. aerial image data input processing, S 20... 3D point group creation processing, S 30. Image data creation processing, S40... Crown height image data creation processing unit, S50... Point extraction processing, S51... Averaging processing, S52... Initial value setting processing, S53... Crown high point cloud data creation processing by hierarchy, S54. Ni = Hi calculation processing, S56... Hi> 0 calculation processing, S57... Ni = Ni + 1 calculation processing, S60... Precision tree image data generation processing, S70. S80: Forest resource information calculation process, S81: Chest height diameter calculation process, S82: Material accumulation determination process, S83: Forest resource information aggregation process, A1, A2, A3, ... First-class tree crown High point cloud region, a1, a2, a3... Maximum value of the first hierarchical crown high point cloud region, B1, B2, B3,..., Second hierarchical crown high point cloud region, b1, b2, b3.・ Maximum value of the second-tier tree crown high point group region, C1, C2, C 3,... Third-tier tree crown high point group region, c1, c2, c3... Maximum value of third-tier tree crown high point group region, Hmxx. Hierarchy width, Ni: Current processing target layer, Hi: Maximum crown height in the next processing target layer

Claims (22)

A forest resource information calculation method for creating forest resource information related to a forest area to be surveyed,
A survey target forest area image data creation processing step of creating survey target forest area image data based on the three-dimensional point cloud data and geographical information corresponding to the region including the survey target forest area;
Based on the survey target forest area image data, a crown height image data creation processing step for creating crown height image data representing the height of the crown of each tree existing in the survey target forest area as a crown height;
Of the crown heights obtained from the crown height image data, a hierarchy having a maximum crown height is defined as a first hierarchy, and a hierarchy having a hierarchy width of a predetermined length starting from the maximum crown height is defined as a ground surface from the first hierarchy. A plurality of tree vertex extraction processing steps for extracting tree vertices of a tree crown having tree vertices in each hierarchy including the first hierarchy for each hierarchy,
Forest resource information calculation method characterized by having In the forest resource information calculation method according to claim 1,
The crown height image data creation processing step creates meshed digital surface model data and digital elevation model data based on the survey target forest area image data, and the digital surface layer model data and the digital digital data for each mesh A forest resource information calculation method characterized by creating crown height image data in which a crown height is obtained for each mesh by taking a difference from elevation model data. In the forest resource information calculation method according to claim 2,
In the hierarchical tree vertex extraction processing step,
Using the crown height image data for which the crown height is obtained for each mesh, the tree-specific crown high point cloud data corresponding to individual crowns existing in each hierarchy including the first hierarchy is created for each hierarchy. Tree-based high point cloud data creation processing step by hierarchy,
From the hierarchical crown high point cloud data corresponding to the individual crowns created by the hierarchical crown high point cloud data creation processing step, the maximum value of the hierarchical crown high point cloud data corresponding to the individual crowns is set as the maximum value. Tree vertex calculation processing step that calculates for each hierarchy, and uses the calculated maximum value as the tree vertex of the crown corresponding to the crown high point cloud data by the hierarchy,
Forest resource information calculation method characterized by including In the forest resource information calculation method according to claim 3,
In the tree vertex calculation processing step, when calculating the maximum value, among the tree-specific crown high point group data corresponding to the individual tree crowns, the tree-specific tree crown high point group data has already been created. A forest resource information calculation method characterized in that the maximum value is calculated by excluding hierarchical crown high point cloud data corresponding to the same crown as the crown. In the forest resource information calculation method according to claim 3 or 4,
In the hierarchical tree vertex extraction processing step,
Each mesh of the crown height image data is set as a processing target mesh, an averaging filter is superimposed on the processing target mesh, and an average value of the crown height of each mesh included in the averaging filter is determined as a crown height of the processing target mesh. And further including an averaging processing step for creating noise-removed canopy height image data by sequentially performing the processing in each processing target mesh,
The hierarchical crown height point group data creation processing step uses the noise-removed crown height image data created by the averaging processing step as the crown height image data for which the crown height is obtained for each mesh. Forest resource information calculation method characterized by creating tree-based crown high point cloud data. In the forest resource information calculation method according to any one of claims 1 to 5,
The forest resource information calculation method, wherein the layer width is set to a length in a range of 0.2 m to 2.0 m. In the forest resource information calculation method according to any one of claims 1 to 6,
The three-dimensional point cloud data is created by SFM (Structure from Motion), which is a three-dimensional shape restoration technique, from photographed image data obtained by photographing images while overlapping the photographing locations from above. Forest resource information calculation method characterized by In the forest resource information calculation method according to any one of claims 1 to 6,
The forest resource information calculation method, wherein the three-dimensional point cloud data is created based on laser measurement data obtained by irradiating a laser beam from above. In the forest resource information calculation method according to any one of claims 1 to 8,
Based on the crown vertices extracted for each hierarchy, the canopy height of the canopy is used to divide the adjacent crowns into regions, using the area division algorithm to divide the individual crowns. A forest resource information calculation method further comprising a precision canopy image data creation processing step for creating image data. In the forest resource information calculation method according to claim 9,
The forest resource information calculation method, wherein the region segmentation algorithm is a Watershed algorithm. In the forest resource information calculation method according to claim 9 or 10,
From the precise canopy image data created by the precise canopy image data creation processing step, precise canopy information creation processing step for creating precise canopy information corresponding to each tree,
Forest resource information calculation processing step for creating forest resource information related to the forest area to be investigated based on the precise crown information corresponding to each tree created by the precise canopy information creation processing step;
Further comprising
The precise crown information corresponding to each tree includes at least the label number of the crown corresponding to each tree, the crown position of the crown corresponding to each tree, the crown diameter of the crown corresponding to each tree, and each tree. The crown area of the crown corresponding to
The forest resource information calculation processing step includes
Chest height diameter calculation processing step for calculating the diameter of the chest height of a human in each tree as the breast height diameter of each tree;
A material integration determination processing step for calculating the volume of each tree,
Each tree obtained based on the chest height diameter of each tree, the volume of each tree, the precise crown information created in the precise crown information creation processing step, and the tree vertices extracted in the tree-specific tree vertex extraction step A forest resource information calculation method comprising: a forest resource information aggregation processing step for performing a process of aggregating tree heights. A forest resource information calculation device for creating forest resource information about a forest area to be surveyed,
A survey target forest area image data creation unit that creates survey target forest area image data based on the three-dimensional point cloud data and geographical information corresponding to the area including the survey target forest area;
Based on the survey target forest area image data, a crown height image data creation unit for creating crown height image data representing the height of the crown of each tree existing in the survey target forest area as a crown height;
Of the canopy heights obtained from the canopy height image data, a hierarchy having a maximum crown height is defined as a first hierarchy, and a hierarchy having a hierarchy width of a predetermined length starting from the maximum crown height is defined as a ground surface from the first hierarchy. And a tree-specific tree vertex extraction unit that extracts tree vertices of a tree crown having tree vertices in each hierarchy including the first hierarchy for each of the hierarchies. Forest resource information calculation device. The forest resource information calculation apparatus according to claim 12,
The crown height image data creation unit creates meshed digital surface model data and digital elevation model data based on the survey target forest area image data, and the digital surface layer model data and the digital elevation for each mesh. A forest resource information calculation device that creates crown height image data in which a crown height is obtained for each mesh by taking a difference from model data. In the forest resource information calculation device according to claim 13,
In the hierarchical tree vertex extraction unit,
Using the crown height image data for which the crown height is obtained for each mesh, the tree-specific crown high point cloud data corresponding to individual crowns existing in each hierarchy including the first hierarchy is created for each hierarchy. A tree-based crown high-point cloud data creation unit,
From the tree-based crown high point group data corresponding to the individual crowns created by the tree-specific crown high point group data creation unit, the maximum value of the tree-based crown high point group data corresponding to the individual canopies is set to the hierarchy. A tree apex calculation unit that calculates the maximum value as the tree apex of the tree crown corresponding to the tree-based crown high point cloud data for each hierarchy,
Forest resource information calculation device characterized in that is included. In the forest resource information calculation apparatus according to claim 14,
When calculating the maximum value, the tree apex calculation unit is a tree crown in which the tree-based crown high point group data has already been created among the tree-specific tree crown high point group data corresponding to the individual tree crowns. A forest resource information calculating apparatus, wherein the maximum value is calculated by excluding hierarchical crown high point cloud data corresponding to the same tree canopy. In the forest resource information calculation apparatus according to claim 14 or 15,
In the hierarchical tree vertex extraction unit,
Each mesh of the crown height image data is set as a processing target mesh, an averaging filter is superimposed on the processing target mesh, and an average value of the crown height of each mesh included in the averaging filter is determined as a crown height of the processing target mesh. In addition, an averaging processing unit for creating noise-removed crown height image data is further included by sequentially performing the processing to be performed on each processing target mesh,
The tree-specific crown high point group data creation unit uses the noise-removed crown height image data created by the averaging processing unit as the crown height image data for which the crown height is obtained for each mesh. Forest resource information calculation device characterized by creating separate tree crown high point cloud data. In the forest resource information calculation device according to any one of claims 12 to 16,
The layer width is set to a length in the range of 0.2 m to 2.0 m. In the forest resource information calculation device according to any one of claims 12 to 17,
The three-dimensional point cloud data is created by SFM (Structure from Motion), which is a three-dimensional shape restoration technique, from photographed image data obtained by photographing images while overlapping the photographing locations from above. Forest resource information calculation device characterized by In the forest resource information calculation device according to any one of claims 12 to 17,
The forest resource information calculation apparatus, wherein the three-dimensional point cloud data is created based on laser measurement data obtained by irradiating laser light from above. In the forest resource information calculation apparatus according to any one of claims 12 to 19,
Based on the crown vertices extracted for each hierarchy, the canopy height of the canopy is used to divide the adjacent crowns into regions, using the area division algorithm to divide the individual crowns. A forest resource information calculation device further comprising a precision canopy image data creation unit for creating image data. In the forest resource information calculation device according to claim 20,
The forest resource information calculation apparatus, wherein the region division algorithm is a Watershed algorithm. In the forest resource information calculation device according to claim 20 or 21,
From the precise crown image data created by the precise crown image data creation unit, a precise crown information creation unit that creates precise crown information corresponding to each tree;
A forest resource information calculation unit that creates forest resource information related to the forest area to be investigated, based on the precise canopy information corresponding to each tree created by the precise canopy information creation unit;
Further comprising
The precise crown information corresponding to each tree includes at least the label number of the crown corresponding to each tree, the crown position of the crown corresponding to each tree, the crown diameter of the crown corresponding to each tree, and each tree. The crown area of the crown corresponding to
In the forest resource information calculation section,
A breast height diameter calculating unit for calculating a diameter of a breast height of a human in each tree as a breast height diameter of each tree;
A material integration fixed part for calculating the volume of each tree,
Chest height diameter of each tree, volume of each tree, height of each tree obtained based on the precise crown information created by the precise crown information creation unit and the tree vertex extracted by the tree-specific tree vertex extraction unit And a forest resource information calculation unit that performs a process of counting the amount of the forest resource information.