Logging decision system and logging decision method

Abstract

When felling trees, it is important to determine which trees to fell and in what direction to fell them. [Solution] The logging decision system 10 is a system that determines a candidate tree for logging and the logging direction of said tree from a plurality of standing trees present in a forest, and comprises: an acquisition unit 11 that acquires 3D data showing the shape and position of the leaf portion for each of the plurality of standing trees; a standing tree determination unit 12 that determines a candidate tree for logging according to the amount of leaves of each of the plurality of standing trees based on the 3D data acquired by the acquisition unit 11; and a logging direction determination unit 13 that determines the logging direction for the candidate tree for logging determined by the standing tree determination unit 12 according to the positional relationship between the candidate tree for logging and the leaves of standing trees other than the candidate tree for logging, as shown by the 3D data.

Description

Technical Field

[0001] The present invention relates to a felling decision system and a felling decision method for determining a felling candidate tree and a felling direction of the tree from a plurality of standing trees included in a forest.

Background Art

[0002] Conventionally, when felling standing trees such as Japanese cedar, defoliation may be carried out (for example, see Patent Document 1). Defoliation is a traditional forestry practice in which the felled standing tree is left in the mountain forest for a certain period of time (2 to 4 months) with branches and leaves attached. The advantages of defoliation are that the bark becomes difficult to peel off and damage during transportation can be reduced, the moisture content of the sapwood with a high moisture content can be lowered and the overall moisture distribution can be made uniform to provide wood with less cracking, splitting, and warping, the color gloss of the wood can be improved, and the heartwood colors with a large variation can be made uniform. As a result, the wood subjected to defoliation is likely to be priced highly in the market. In addition, since moisture evaporates and the weight is reduced to about 70%, it is possible to reduce the energy (CO2) during unloading, as well as reduce the fuel oil and time related to drying after sawing (reduction of drying cost).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When felling standing trees from a forest, a tree to be felled and a felling direction are determined from a plurality of standing trees existing in the forest. When the tree to be felled is a target for defoliation, defoliation needs to be considered in these determinations. These determinations have been made based on the experience or intuition of the feller. However, defoliation has not always been appropriately considered in these determinations. Therefore, the determined tree and felling direction have not always been appropriate.

[0005] The present invention has been made in view of the above, and aims to provide a tree felling decision system and a tree felling decision method that can appropriately determine which trees to fell and the direction of felling when felling standing trees. [Means for solving the problem]

[0006] To achieve the above objective, the logging decision system according to the present invention is a logging decision system that determines a candidate tree for logging and the logging direction of said tree from a plurality of standing trees present in a forest, comprising: acquisition means for acquiring 3D data showing the shape and position of the leaf portion for each of the plurality of standing trees; standing tree determination means for determining a candidate tree for logging according to the amount of leaves of each of the plurality of standing trees based on the 3D data acquired by the acquisition means; and logging direction determination means for determining the logging direction for the candidate tree for logging determined by the standing tree determination means, according to the positional relationship between the candidate tree for logging and the leaves of standing trees other than the candidate tree, as shown by the 3D data.

[0007] In the tree felling decision system according to the present invention, candidates for felling are determined based on the amount of foliage of each of several standing trees. Furthermore, for the candidates for felling, the felling direction is determined based on the positional relationship between the candidate tree and the leaves of other standing trees. In this way, the tree felling decision system according to the present invention appropriately considers the foliage of the standing trees when determining which trees to fell and in what direction. Therefore, when felling trees, the tree felling decision system according to the present invention makes it possible to appropriately determine which trees to fell and in what direction.

[0008] The acquisition means may acquire 3D data showing the shape of the trunk for each of the multiple standing trees, and the standing tree determination means may determine which standing trees are candidates for felling based on the value of each of the multiple standing trees based on the 3D data acquired by the acquisition means. With this configuration, it is possible to determine which standing trees to fell appropriately according to their value.

[0009] The acquisition means may acquire 3D data showing the shape of the ground where multiple trees grow, and the felling direction determination means may determine the felling direction according to the shape of the ground where the trees to be felled, as shown by the 3D data, grow. With this configuration, the felling direction can be appropriately determined according to the shape of the ground where the trees to be felled grow.

[0010] Incidentally, in addition to being described as an invention of a logging decision system as described above, the present invention can also be described as an invention of a logging decision method as follows. These are substantially the same invention, differing only in category, and produce similar functions and effects.

[0011] In other words, the logging decision method according to the present invention is a logging decision method which is a method for operating a logging decision system that determines a candidate tree for logging and the logging direction of said tree from a plurality of standing trees present in a forest, and includes: an acquisition step of acquiring 3D data showing the shape and position of the leaf portion for each of the plurality of standing trees; a standing tree determination step of determining a candidate tree for logging according to the amount of leaves of each of the plurality of standing trees based on the 3D data acquired in the acquisition step; and a logging direction determination step of determining the logging direction for the candidate tree for logging determined in the standing tree determination step according to the positional relationship between the candidate tree for logging, as shown by the 3D data, and the leaves of standing trees other than the candidate tree for logging. [Effects of the Invention]

[0012] According to the present invention, when felling trees, it is possible to appropriately determine which trees to fell and the direction of felling. [Brief explanation of the drawing]

[0013] [Figure 1] This diagram shows the configuration of a logging decision system according to an embodiment of the present invention. [Figure 2] This figure shows an example of 3D data used by the logging decision system. [Figure 3] This table shows an example of price data used by the logging decision system. [Figure 4] This diagram schematically shows the curvature of a log after it has been cut into sections, known as the "yataka" (arrow height). [Figure 5] This is a diagram to explain how to determine the direction in which to cut down standing trees. [Figure 6] This is a flowchart showing a logging decision method, which is a process performed in the logging decision system according to an embodiment of the present invention. [Modes for carrying out the invention]

[0014] The embodiments of the logging decision system and logging decision method according to the present invention will be described in detail below with reference to the drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and redundant explanations are omitted.

[0015] Figure 1 shows the logging decision system 10 according to this embodiment. The logging decision system 10 is a system (device) that determines which trees are candidates for logging and which trees are to be logged from among a plurality of standing trees present in a forest. The trees to be logged are, for example, those that will be cut down and sold. In this case, the logging decision system 10 determines which trees are suitable for being cut down and sold as candidates for logging. However, the logging decision system 10 may determine which trees are candidates for logging and which trees are to be logged based on conditions other than those described above.

[0016] The trees to be felled are those that will undergo leaf-drying after felling. The types of trees are, for example, Japanese cedar or cypress. As will be described later, the felling decision system 10 makes the above decision taking leaf-drying into consideration. For example, the user fells the trees by referring to the decision made by the felling decision system 10. Although the felling decision system 10 determines the trees to be felled, since the final decision rests with the feller, if the feller follows the decision made by the felling decision system 10, the felling decision system 10 will decide which trees to fell.

[0017] The logging decision system 10 is specifically a computer including hardware such as a CPU (Central Processing Unit) and a memory. Each of the functions of the logging decision system 10 described below is exhibited when these components operate according to a program or the like. Note that the logging decision system 10 may be realized by one computer, or may be realized by a computer system configured by connecting a plurality of computers to each other via a network. The logging decision system 10 may have a communication function for acquiring information necessary for the processing of the logging decision system 10 shown below.

[0018] Subsequently, the functions of the logging decision system 10 according to the present embodiment will be described. As shown in FIG. 1, the logging decision system 10 includes an acquisition unit 11, a standing tree determination unit 12, and a logging direction determination unit 13.

[0019] The acquisition unit 11 is an acquisition means for acquiring three-dimensional data indicating the shape and position of the leaf part for each of a plurality of standing trees. The acquisition unit 11 may acquire three-dimensional data indicating the shape of the trunk for each of a plurality of standing trees. The acquisition unit 11 may acquire three-dimensional data indicating the shape of the ground where a plurality of standing trees grow.

[0020] The acquisition unit 11 performs acquisition, for example, as follows. The acquisition unit 11 acquires three-dimensional data of the positions of objects in a forest where a plurality of standing trees to be determined in the present embodiment exist. The three-dimensional data is data (comprehensive point cloud data) indicating the coordinates where an object is located in a three-dimensional space. The three-dimensional data can be obtained by performing measurement on-site by an existing technique such as LiDAR (Light Detection And Ranging). The device used for measurement on-site may be any of a stationary type, a backpack type, and a drone-mounted type. FIG. 2 shows an example in which the three-dimensional data is illustrated. One point in FIG. 2 corresponds to the position corresponding to the object.

[0021] The 3D data may include other information obtained by existing technologies such as LiDAR. For example, the 3D data may include the reflectance for each 3D coordinate. The reflectance is information indicating the reflectance of light used for measurement from the position at which the measurement was performed. The reflectance may also be information for each return number (the number of reflections from that position). This information may be used for the following processing.

[0022] 3D data acquisition is performed, for example, by receiving 3D data input operations from a user to the logging decision system 10, or by receiving 3D data transmitted from another device. Alternatively, 3D data acquisition may be performed by other methods.

[0023] The acquisition unit 11 classifies the type of object whose location is indicated by the acquired 3D data. The classification of the type is performed for each location in the 3D data. The types to be classified are, for example, the ground, the trunks and branches of standing trees (which are the subject of the determination), and shrubs (which are not the subject of the determination). This classification can be performed using existing technologies such as 3D data identification using machine learning. The classification of standing tree trunks and branches is performed separately for each standing tree, and an identifier (identification ID) is assigned to each standing tree to which the trunks and branches of the classified standing tree belong, in order to identify that standing tree.

[0024] The 3D data, after the above classification, shows the shape and position of the leaf portion (corresponding to the branch and leaf portion above) and the shape of the trunk for each of the multiple standing trees, and also shows the shape of the ground where the multiple standing trees grow. In subsequent processing in the logging decision system 10, the classified 3D data is used for processing. In the following descriptions of the standing tree determination unit 12 and the logging direction determination unit 13, the 3D data is the 3D data that has been classified as above.

[0025] The acquisition unit 11 may acquire 3D data that has been classified as described above by another system. Furthermore, the acquisition unit 11 may acquire 3D data other than those described above as 3D data indicating the shape and position of the leaf portion. For example, the acquisition unit 11 may acquire conventional digital elevation model (DEM) data (e.g., 5m DEM data from public surveys or independently surveyed DEM data) for the 3D data of the terrain (ground) where the standing trees are located. The acquisition unit 11 outputs the acquired 3D data to the standing tree determination unit 12 and the felling direction determination unit 13.

[0026] The tree determination unit 12 is a tree determination means that determines which trees to fell based on the amount of foliage of each of the multiple trees based on the 3D data acquired by the acquisition unit 11. The tree determination unit 12 may also determine which trees to fell based on the value of each of the multiple trees based on the 3D data acquired by the acquisition unit 11.

[0027] The tree selection unit 12 performs the selection as follows, for example: The tree selection unit 12 receives 3D data from the acquisition unit 11. The tree selection unit 12 calculates (generates) information used to determine which trees are candidates for felling from the input 3D data. Specifically, the tree selection unit 12 calculates the amount of leaves and value for each tree.

[0028] The tree determination unit 12 calculates the volume of the branches and leaves as the leaf volume for each tree indicated by the 3D data. The branches and leaves are generated, for example, by connecting the outer points of the points classified as branches and leaves to form a polygon region. The generation of polygon regions for 3D data and the calculation of the volume of said regions can be performed using existing technologies. Alternatively, the tree determination unit 12 may count the positions of the branches and leaves (positions identified as branches and leaves) indicated by the 3D data for each tree and calculate the value obtained from the count as the leaf volume. Furthermore, the tree determination unit 12 may calculate the leaf volume of a tree by methods other than those described above, as long as the calculation is based on 3D data.

[0029] The standing tree determination unit 12 calculates the value of each standing tree indicated by the 3D data. The calculated value of the standing tree is, for example, the price at which felled standing trees are bought and sold. Normally, when standing trees are bought and sold, the felled trees are cut into lengths that are predetermined as trading units to become logs. Therefore, the standing tree determination unit 12 calculates the value of the standing trees assuming that they have been cut into logs after being felled.

[0030] Typically, the buying and selling price of a log after it has been cut into sections is determined by its length (m) and diameter (e.g., diameter class of the top end (cm)). For example, as shown in the table in Figure 3, a buying and selling price (e.g., a cubic meter price (yen) as shown in Figure 3) is set for each combination of log length (m) and diameter (diameter class of the top end (cm)) shown in the table in Figure 3, and the corresponding buying and selling price are stored in advance as price data for calculating the value of the standing timber. Note that although only lengths of 3 (m) and 4 (m) are shown in the table in Figure 3, other lengths are usually also subject to buying and selling.

[0031] Alternatively, the timber determination unit 12 may store price data for each type of timber (for example, cedar or cypress) and calculate the value of each type of timber (for example, cedar or cypress). In this case, the acquisition unit 11 acquires information indicating the type of timber, and the timber determination unit 12 calculates the value of the timber using the price data of the type indicated by that information.

[0032] The standing tree determination unit 12 generates a combination of multiple logs after cutting the standing tree, based on the shape of the tree trunk shown by the 3D data for each standing tree. At this time, the length and thickness of the logs after cutting are made to match the price data stored in the standing tree determination unit 12. In other words, the logs after cutting are made to be in units that can be bought and sold.

[0033] The tree determination unit 12 identifies the trunk direction, which is the direction in which the trunk of a standing tree extends, from the 3D data. The tree determination unit 12 calculates the combinations of logs that can be obtained when the standing tree is cut (logged) in the trunk direction. Usually, there are multiple such combinations. The tree determination unit 12 may calculate all combinations, or it may calculate only some combinations that meet certain conditions. For example, in the case of a 17m standing tree, (1) a combination of 8m, 4m, 3m, and 2m, and (2) a combination of 4m, 4m, 4m, and 5m are calculated.

[0034] When calculating combinations, the standing tree determination unit 12 may pre-store the cutting allowance (cm) from the base when felling a standing tree and the cutting allowance (cm) when cutting into logs, and calculate combinations of logs that take these cutting allowances (buffers) into consideration.

[0035] The standing tree determination unit 12 calculates the buying and selling prices of the logs included in a combination from pre-stored price data. The standing tree determination unit 12 sums up the buying and selling prices of the logs for each combination. The standing tree determination unit 12 uses the value of the log as the value of the combination with the highest total buying and selling price. If there is only one combination of logs for a single standing tree, the standing tree determination unit 12 only needs to use the sum of the buying and selling prices of the logs in that combination as the value of the tree.

[0036] If a log has a certain degree of curvature after being cut into sections, it is usually not possible to sell it. The standing tree determination unit 12 may calculate the value of the standing tree taking into account the curvature of the log after cutting. For example, the standing tree determination unit 12 calculates the arrow height shown in Figure 4 from the 3D data for each log after cutting included in the above combination. The standing tree determination unit 12 generates a column shape (a cylindrical shape if the cross-section is circular) with the two cross-sections of the log after cutting as end faces from the 3D data. The standing tree determination unit 12 defines the arrow height as the longest length from the surface of the column shape to the surface of the log inside the column shape.

[0037] The standing timber determination unit 12 determines whether each log after cutting included in the above combination is eligible for sale based on the calculated yarrow height. Alternatively, this determination may be made based on the thickness of the log after cutting (for example, the diameter of the top of the log as shown below). For example, the standing timber determination unit 12 calculates the value indicating the curvature of the log as yarrow height h (cm) / diameter of the top of the log after cutting (cm) × 100. The standing timber determination unit 12 compares the calculated h / d × 100 with a pre-set threshold, and determines that the log is not eligible for sale if the calculated h / d × 100 is greater than or equal to the threshold. The standing timber determination unit 12 excludes combinations containing logs that have been determined not to be eligible for sale from the combinations used to calculate the value of the standing timber. Alternatively, the standing timber determination unit 12 may exclude logs that have been determined not to be eligible for sale from the combination and calculate the total sale price for that combination.

[0038] Furthermore, the tree determination unit 12 may calculate the value of the trees using methods other than those described above, provided that the calculation is based on three-dimensional data.

[0039] The tree selection unit 12 determines trees to be felled based on the calculated information and pre-stored selection criteria. For example, the tree selection unit 12 stores selection criteria for each type of information and determines trees that satisfy the selection criteria for all types of information as trees to be felled.

[0040] From the perspective of performing leaf-drying after felling, it is desirable that the trees to be felled have a sufficient amount of foliage to allow for proper leaf-drying. From this perspective, the tree determination unit 12 sets a threshold value (for example, 0.3m) that is predetermined as a determination criterion for the amount of foliage. 3 The system stores the calculated amount of leaves on the standing tree. The standing tree determination unit 12 compares the calculated amount of leaves on the standing tree with the threshold value, and if the amount of leaves on the standing tree is equal to or greater than the threshold value, it determines that the criteria for determining the amount of leaves are met.

[0041] It is desirable that the trees to be felled are of high value (for example, have a high selling price). From this perspective, the tree determination unit 12 stores a predetermined threshold (for example, 10,000 yen) as a criterion for determining the value of the trees. The tree determination unit 12 compares the calculated value of the trees with the threshold, and if the value of the trees is equal to or greater than the threshold, it determines that the criterion for determining the value of the trees is met.

[0042] Furthermore, the tree selection unit 12 may also determine trees to be felled based on information other than that mentioned above. In this case, the acquisition unit 11 can acquire this information using existing technology. For example, the tree selection unit 12 may use the amount of solar radiation on the trees (for example, the amount of solar radiation at the base of the trees) and a preset threshold value (for example, 100 MJ / m²) to determine the trees to be felled. 2 The solar radiation is determined to be satisfied if it is equal to or greater than the threshold value when compared with the given value. Solar radiation can be calculated using existing technologies. For example, solar radiation can be obtained as raster data per mesh calculated from a digital elevation model (DEM) or digital surface model (DSM).

[0043] Furthermore, the tree determination unit 12 may decide whether or not to cut down trees in each area of ​​the forest (for example, a pre-set sub-compartment of about 5 hectares). For example, the tree determination unit 12 compares the proportion and value of the number of trees in the area with a pre-set and stored threshold (for example, 100 trees / ha, 15,000 yen / tree), and decides to cut down trees in the area if the proportion and value are equal to or greater than the threshold.

[0044] The tree selection unit 12 may make decisions based on criteria other than those mentioned above, as long as it determines which trees are candidates for felling based on the amount of foliage of each of the multiple trees based on 3D data. The tree selection unit 12 inputs information indicating the trees that have been determined to be candidates for felling into the felling direction determination unit 13.

[0045] The felling direction determination unit 13 is a felling direction determination means that determines the felling direction for a standing tree that is a candidate for felling, as determined by the standing tree determination unit 12, according to the positional relationship between the standing tree that is a candidate for felling, as shown by 3D data, and the leaves of other standing trees. The felling direction determination unit 13 may also determine the felling direction according to the shape of the ground where the standing tree that is a candidate for felling, as shown by 3D data, is growing.

[0046] The felling direction determination unit 13 makes a decision, for example, as follows: The felling direction determination unit 13 receives 3D data from the acquisition unit 11. The felling direction determination unit 13 receives information from the standing tree determination unit 12 indicating the standing trees that have been determined to be candidates for felling.

[0047] The felling direction determination unit 13 calculates the slope direction of the ground relative to the horizontal plane from 3D data, based on the location of the ground where the candidate trees for felling, indicated by the information input from the standing tree determination unit 12, are growing. The calculation of the slope direction based on the 3D data of the ground can be performed using existing technology.

[0048] The felling direction determination unit 13 identifies the direction (e.g., orientation) on the horizontal plane where the slope is steepest and is uphill from the ground where the trees to be felled are growing, based on the calculated slope direction. The felling direction determination unit 13 sets a range of directions for determining the felling direction based on the identified direction. For example, the felling direction determination unit 13 determines the range of directions on the horizontal plane within 90° to the left and right of the identified direction as the range for determining the felling direction. This is because, when drying the leaves, it is best to fell in the direction where the ground is uphill. For example, if the forest where the trees are located is on a mountain, it is best to fell in the direction towards the mountaintop or ridge.

[0049] Next, the felling direction determination unit 13 divides the horizontal area where the trees are growing into meshes (for example, 50cm x 50cm meshes), as shown in Figure 5. The felling direction determination unit 13 calculates the amount of foliage for each mesh from the 3D data. The calculated amount of foliage may be for each tree. The calculation of the amount of foliage for each mesh may be performed in the same way as the calculation of the amount of foliage by the tree determination unit 12. Alternatively, the tree determination unit 12 may calculate the amount of foliage for each mesh. The values ​​for each mesh in Figure 5 show the calculated amount of foliage for each mesh. Also, the areas shown by the thick lines in Figure 5 show the approximate areas for each tree.

[0050] The felling direction determination unit 13 determines the felling direction of a standing tree from the range of directions set above, using the amount of leaves for each mesh. The determined felling direction is the direction in which, when a line is drawn from the position of the trunk of the standing tree candidate to be felled along the length of the trunk, the sum of the amount of leaves in the meshes passing through that line is the smallest among the directions in the above range. In other words, the felling direction of the standing tree is determined to be the direction in which the influence of the leaves of the standing tree is minimized when the trunk falls due to felling. This determination can be made using conventional methods (for example, a method using an 8-direction (D8) flow model). Note that the above determination of the felling direction may be made without using the amount of leaves for the standing tree candidate to be felled.

[0051] According to this determination method, for example, the felling direction (direction of cutting down) will be as shown in Figure 5. By felling trees in this direction, it is possible to prevent sunlight from being obstructed to the felled trees by the leaves of other trees. Felling trees in this direction reduces the number of trees that get caught on other trees compared to felling in other directions. Trees that get caught on other trees reduce work efficiency and pose a risk of workers being trapped underneath if the felled tree bounces up after being hit and falls in an unpredictable direction. Therefore, reducing the number of trees that get caught on other trees is important from the standpoint of work efficiency and safety.

[0052] The felling direction determination unit 13 may make decisions other than those described above, as long as it determines the felling direction based on the positional relationship between the standing trees that are candidates for felling, as indicated by the 3D data, and the leaves of standing trees other than those candidates for felling.

[0053] The felling direction determination unit 13 outputs information indicating the standing trees and felling direction determined by the felling decision system 10. In addition, the felling direction determination unit 13 may output information indicating the details of the bucking process when the value of the standing trees was calculated by the standing tree determination unit 12, along with the above determination result. For example, the felling direction determination unit 13 transmits this information to the user's terminal. Alternatively, the felling direction determination unit 13 displays this information on a display device provided by the felling decision system 10. Furthermore, the felling direction determination unit 13 may output this information by methods and to output destinations other than those described above.

[0054] Users can refer to the outputted information when felling trees. For example, they can compare the outputted information with the actual trees using a portable GIS (Geographic Information System) to confirm the felling direction and cutting details. Users can also refer to the outputted information to conduct preliminary studies on whether or not to actually fell the trees and to consider the felling process. The above describes the functions of the felling decision system 10 according to this embodiment.

[0055] Next, the logging decision method, which is the process (operation method performed by the logging decision system 10) executed by the logging decision system 10 according to this embodiment, will be explained using the flowchart in Figure 6. In this process, the acquisition unit 11 acquires 3D data indicating the shape and position of the leaf portion for each of the multiple standing trees (S01, acquisition step). The standing tree determination unit 12 determines the standing trees to be felled according to the amount of leaves of each of the multiple standing trees based on the 3D data acquired by the acquisition unit (S02, standing tree determination step). Subsequently, the logging direction determination unit 13 determines the felling direction for the standing trees to be felled determined by the standing tree determination unit 12, according to the positional relationship between the standing trees to be felled, as shown by the 3D data, and the leaves of standing trees other than the standing trees to be felled (S03, felling direction determination step). Subsequently, the felling direction determination unit 13 outputs information indicating the determined content (standing trees to be felled and felling direction) (S04). The above is the logging decision method according to this embodiment.

[0056] In this embodiment, the trees to be felled are determined according to the amount of foliage of each of the multiple trees. Furthermore, for the trees to be felled, the felling direction is determined according to the positional relationship between the leaves of the trees to be felled and the leaves of the other trees. In this way, in this embodiment, the leaves of the trees are appropriately considered when determining which trees to fell and in what direction. Therefore, according to this embodiment, when felling trees, it is possible to appropriately determine which trees to fell and in what direction. For example, for trees that will undergo leaf-drying after felling, it is possible to appropriately determine which trees to fell and in what direction without relying on the feller's experience or intuition or time-consuming consideration.

[0057] As in the embodiment described above, the determination of trees to be felled may also be carried out in accordance with the value of each of the multiple trees based on 3D data. With this configuration, it is possible to determine which trees to fell appropriately in accordance with the value of the trees. For example, trees that will fetch a high price can be determined as trees to be felled. However, the determination of trees to be felled does not necessarily have to be carried out in accordance with the value of the trees.

[0058] As in the embodiment described above, the direction of felling may also be determined in accordance with the shape of the ground where the trees to be felled are growing, as indicated by the 3D data. With this configuration, the direction of felling can be appropriately determined in accordance with the shape of the ground where the trees to be felled are growing. However, the direction of felling does not necessarily have to be determined in accordance with the shape of the ground where the trees are growing. [Explanation of symbols]

[0059] 10...Logging decision system, 11...Acquisition unit, 12...Standing tree selection unit, 13...Logging direction determination unit.

Claims

1. A logging decision system that determines which trees are candidates for felling and the direction of felling for those trees from among multiple standing trees present in a forest, For each of the aforementioned multiple standing trees, an acquisition means is provided to acquire three-dimensional data indicating the shape and position of the leaf portion. A tree determination means that determines which trees are candidates for felling based on the amount of leaves of each of the plurality of trees based on the three-dimensional data acquired by the acquisition means, With respect to the trees designated as candidates for felling by the aforementioned tree determination means, a felling direction determination means determines the felling direction according to the positional relationship between the trees designated as candidates for felling, as shown by the three-dimensional data, and the leaves of trees other than those designated as candidates for felling. A logging decision system equipped with the following features.

2. The acquisition means acquires three-dimensional data showing the shape of the trunk for each of the plurality of standing trees, The logging decision system according to claim 1, wherein the standing tree determination means determines standing trees to be felled according to the value of each of the plurality of standing trees based on the three-dimensional data acquired by the acquisition means.

3. The acquisition means acquires three-dimensional data showing the shape of the ground where the plurality of trees grow, The logging direction determination means determines the logging direction according to claim 1 or 2, also in accordance with the shape of the ground on which the candidate trees for logging, as indicated by the three-dimensional data, grow.

4. A logging decision method is a method for operating a logging decision system that determines which trees are candidates for logging and the direction of logging for those trees from among multiple standing trees present in a forest, The acquisition step involves acquiring three-dimensional data showing the shape and position of the leaf portion for each of the aforementioned multiple standing trees, A tree determination step in which candidates for felling are determined based on the amount of foliage of each of the multiple trees based on the three-dimensional data acquired in the acquisition step, In the tree selection step, a felling direction determination step is performed to determine the felling direction for the trees selected as candidates for felling in the aforementioned tree selection step, according to the positional relationship between the trees selected as candidates for felling, as shown by the three-dimensional data, and the leaves of trees other than those selected for felling. A logging decision method that includes this.

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