• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The invention relates to a method for the selective harvesting of timber in a forest stand and to a corresponding device (1) for precipitation and / or estrangement, which includes a vehicle (F) and a receiving module (3) and a controller (4) which can access data , and the vehicle (F) is moved through the forest stand and selectively falls and branches, and the pickup module (3) detects the stock to assist the navigation of the vehicle (F). The task is to improve the optimization of harvesting and to minimize errors. This object is achieved in that the recording module (3) detects at least one decision parameter and the control (4) thus makes a decision in real time for selective precipitation and estrangement.
    公开号:AT520253A2
    申请号:T50607/2018
    申请日:2018-07-16
    公开日:2019-02-15
    发明作者:
    申请人:Umweltdata G M B H；
    IPC主号:
    专利说明:

    SUMMARY
    The invention relates to a method for selective timber harvesting in a forest and an associated device (1) for felling and / or pruning, which a vehicle (F), as well as a recording module (3) and a controller (4) that can access data , and the vehicle (F) is moved through the forest stock and selectively falls and branches, and the recording module (3) detects the stock to support the navigation of the vehicle (F). The task is to improve the optimization of the timber harvest and to minimize errors. This object is achieved according to the invention in that the recording module (3) detects at least one decision parameter and the control (4) thus makes a decision in real time for selective felling and pruning.
    FIG.
    / 20th
    21955AT
    The invention relates to a method for selective wood harvesting in a forest and a device for felling and pruning, which comprises a vehicle, as well as a recording module and a controller that can access data, and the vehicle is moved through the forest and selectively falls and branches and the recording module records the inventory to support the navigation of the vehicle. Selective timber harvesting can pursue different goals, which may be several decades in the future, such as optimizing the timber yield, increasing the stability or biodiversity of the forest stand.
    In addition to classic vehicles, vehicles are also machines that are suitable for locomotion, such as spider-like walking machines.
    Such devices and methods for selective timber harvesting in a forest are known, for example, from self-propelled timber harvesting machines, which are also called harvesters in German-speaking countries. These wood harvesting machines can find a tree from the forest stock that is intended for felling, manned or unmanned on the basis of recorded data for navigation. This tree can, for example, be marked in a simple manner beforehand with an iron-containing color and the wood harvester recognizes it, moves to it and falls it.
    For example, if all the necessary forest inventory data is available, it is now possible to make the following decisions:
    At least two measurements are carried out at different times of the diameter of at least one tree, the most relevant conditions for the growth of the tree are known. A future growth diameter is calculated using a forest growth model. The decision to make a precipitation is based on this model. This is done in order to be able to target the trees that are to be felled on the day of the felling.
    / 20th
    This method is very susceptible to errors, since various influencing factors can no longer be taken into account after the last measurement point and the
    The diameter of the trees is usually very different from that determined with the model
    Diameters vary.
    Another possibility is if measurements are carried out immediately or shortly before the precipitation and the precipitation decision is based on this one-time measurement. However, this method does not allow forecasting and optimization with regard to a specific time horizon, as would be possible using the forest growth model given above, for example economic optimization for a time window of twenty years.
    The object of the present invention is to provide a method and a device which allow a good optimization of the selective timber harvest in a forest stand and in which errors are minimized.
    This object is achieved according to the invention by a method mentioned at the outset in that the recording module detects at least one decision parameter and the controller thus makes a decision in real time for selective felling and pruning. This makes it possible to make precise optimizations precisely at the required point in time.
    This means that no cumbersome measurements at several points in time are required, but only a one-time recording is necessary, from which data is generated, as is done with all other methods, and a second recording on the day of the forestry, directly at the forest. The errors and deviations from the model are reduced and this requires maximum flexibility and the highest reaction speed in the event of changed circumstances.
    A device achieves this task with the above-mentioned advantages according to the invention in that the control is connected to the recording module and the vehicle in a signal-conducting manner and has computing power, so that a decision to make a decision - preferably using at least one model - is made in real time.
    It is inexpensive and easy to implement if at least a thickness of a tree is recorded as a decision parameter by the recording module.
    / 20th
    In order to be able to give a better estimate or calculation of the value of a tree, it is advantageous if a height of a tree is
    Decision parameters is recorded by the recording module.
    In order to be able to make better future forecasts, a special variant of the method provides that at least one size of a crown of a tree is recorded as a decision parameter by the recording module.
    This is also possible if the position to neighboring trees of a tree is recorded as a decision parameter by the recording module.
    In order to be able to determine the value of the tree, it is favorable if at least the tree type is determined by the control as a decision parameter from recordings of the recording module.
    It is particularly advantageous if the recording module has a unit for tree ring detection and the number of tree rings and / or tree ring thicknesses of the individual trees of the forest stand are measured by this unit of the recording module. By determining the number of tree rings or the tree ring thickness, it is possible to determine the growth of a tree over the years and also to better model the future growth of another tree or the forest in general.
    It is advantageous if the unit for tree ring detection has at least one sawing tool and at least one optical pickup.
    The tree ring detection unit includes a sawing tool such as a chain saw, which is combined with an optical sensor. The optical sensor detects the number of tree rings near the center of the trunk and the thickness of the tree rings during the cut. The annual growth can be derived from this very precisely.
    It is provided in a special embodiment that the recording module has a unit for taking and analyzing soil samples, and that data processed from / 20 previous recordings - preferably recordings made by the device - of the forest stand are used when processing the recordings by the recording module.
    In order to ensure a quick calculation, especially in remote forest stands, it is advantageous if the control is arranged on the vehicle and the felling decisions are made in the control on the vehicle.
    In order to be able to outsource the high computing power and enable large controls to be kept stationary in an ideal environment with regard to cooling and cleanliness, it is advantageous if the control is arranged outside the vehicle and the vehicle is connected to the control for signal transmission, so that a The decision to make a precipitation can be made in real time and can be transmitted to the vehicle.
    It is particularly advantageous if the decision to make a precipitation is optimized in terms of economy for a given time horizon. This makes it possible to use the possibilities of the method according to the invention in an ideal manner.
    The navigation of the device is made considerably easier if the vehicle has a global navigation satellite system (GNSS) and is used to navigate the vehicle and determine its position.
    In order to generate current data, for example for a forest database, it is provided in a favorable embodiment that the recording module has a laser module and that the geometry and / or the position of tree surfaces are recorded in high resolution.
    A good and inexpensive alternative to this is a variant in which the recording module comprises at least one camera, preferably a digital camera, and the camera is preferably equipped with at least one laser projector, the camera being particularly preferably a stereo camera and if with this camera, preferably during the trip - and / or with a stereo camera, in several directions - preferably in any orientation - images of the forest stand are recorded.
    / 20th
    In order to enable the 3D reconstruction from the image data of the stereo camera better, faster and more accurately, it is favorable if a laser projector during the
    Photo recordings project light points onto the surroundings and the light points into the
    Recordings are taken into account in the image matching process.
    It is favorable if the geometry and position of the tree surfaces and the appearance of the tree surface are evaluated stereophotogrammetrically from the images.
    Alternatively, a good solution is possible if the recording module has a radar module and / or an ultrasound module and with these the geometry and / or the position of the tree surfaces of the forest stand are recorded.
    Recordings of the forest stand are particularly advantageous and accurate if the recording module has a hyperspectral sensor for the visual and automatic detection of tree species, tree vitality or tree damage and the vehicle automatically recognizes tree species and / or tree vitality and / or tree damage with the aid thereof.
    In order to further improve the accuracy of the method for the future, it is advantageous if the data recorded - preferably by means of tree ring detection - is used to improve a forest growth model, this being particularly preferably carried out in real time
    The invention is explained in more detail below with reference to a non-restrictive exemplary embodiment in the figure.
    In the figure, a device 1 for felling and branching is shown, which moves through a forest stand, which is symbolically represented by a single tree. The device 1 comprises a vehicle F, which has a gripper arm 2 and a receiving module 3. The recording module 3 is used to record data from the forest stand. Depending on the version, the recording module 3 comprises a unit for tree ring detection J, a laser module, a stereo camera, an ultrasound module, a hyperspectral sensor and a laser projector for image matching with the images of the stereo camera.
    / 20th
    A device for branching A, which is designed as a saw, is also arranged on the gripping arm 2. This is used to carry out a keying on trees that are still alive.
    Two roller-shaped cutting tools W are provided on the gripping arm 2 for quickly removing the branches after the felling. However, these are unsuitable for groping because they can cause additional bark injuries to the tree.
    In the embodiment shown, the controller 4 is arranged fixedly outside the vehicle F and sends to the vehicle F and receives from the vehicle F the information via a signal connection S, which is indicated by a radio symbol.
    In an alternative embodiment, which is shown in broken lines in the figure, the controller 4 is installed on the vehicle F. This is particularly practical in remote areas, since there the connection between a remote control 4 and the vehicle F may be less simple due to the particular circumstances.
    This controller 4 takes over the evaluation of the measured values and data recorded by the recording module. In addition, the controller 4 calculates a growth forecast using forest growth models and makes adjustments to the forest growth model using already known data that result from previous recordings and forest inventories.
    The method looks, for example, as follows: The vehicle F approaches a first tree and uses the laser module of the recording module 3 to determine the height and the diameter of the tree by measuring the distance. Furthermore, the surroundings of the tree are considered, so its distance to the neighboring trees and the size of its crown are recorded by the recording module. The tree of type A is recognized by the hyperspectral sensor and the data stored in a database. It is also known about it, or it is determined on the basis of the recordings for which use the tree trunk can be used, for example as firewood or as construction wood, and what its current monetary value is. Now, during and after the acquisition of this tree data, a calculation takes place in the controller, how this tree will develop in the future and whether it makes more sense to branch or cut this first tree now or in the future or whether it makes more sense is to give this treatment to a neighboring tree.
    With a spruce tree that is close to a promising Swiss pine or a particularly beautiful veneer oak, the decision to fell is more likely to be given in order to give the veneer oak or Swiss pine more space and thus give it the opportunity for further growth, since its wood has a higher value than the value of a spruce, which is used more as construction wood for construction.
    This decision can be made in such a way that the economic benefit should be maximum for a certain period, for example the next fifty years. In an alternative embodiment, the calculation can also be optimized with regard to a different size, for example the stability of the forest stand against storm damage.
    If a decision is made about the precipitation, the tree rings J can be used to take a picture of the tree rings. The number of annual rings and the thickness of the individual annual rings are determined by an optical sensor.
    For the method according to the invention, measurements are carried out at a time t and at an earlier time (t-1) with the vehicle F of the device 1 before the time of travel (t + 1) and are stored in a database. For example, the diameter dt and dt-1 at time t and at an earlier time (t-1) or the height Ht and Ht-1 and the size Gt and Gt-1 of the crown of the tree are recorded. The conditions of the weather and other factors for the growth wt and wt-1 at time t and at the earlier time (t-1) are known. The known values are used to adapt a suitable forest growth model. The measurement of the values at the time of travel (t + 1) is carried out on the vehicle F: the diameter dt + 1, the height Ht + 1 and the size Gt + 1 of the crown of the tree on the vehicle F are determined by the recording module 3 , these values are entered into the forest growth model and updated daily. Optimization is carried out online in real time by the control system 4. A forest growth model is used, which can be structured as follows, for example:
    / 20 dt + i = a. dt + β. dt-i + y.wt + δ. wt-i
    The Greek letters denote α, β, γ and δ. Parameters which can be adjusted on the basis of the measurements and which determine the properties of the
    Specify forest stock.
    This type of forest growth model is well known in the art. Precipitation decisions are currently being made in such a way that measurements of the diameter are carried out at time t and at an earlier time t-1 and the ratios wt and wt-1 are again known. Then, based on this data, a value is created for the time of the trip based on the forest growth model and the optimized decision on the basis of the precipitation is made on the basis of this. The results from the forest growth model represent only a rough estimate and, depending on the topicality of the measurement data, the decision to make a precipitation can therefore differ greatly from the decision to make a precipitation made by the method according to the invention.
    In a practicable alternative, the measurements are carried out at a point in time before the journey and adjustments are made accordingly. Although this reduces the error, this additional measurement makes it much more complex and costly.
    AI (artificial intelligence) is used in the so-called precision forestry. The device according to the invention provides support for decisions regarding the harvesting of timber, in particular for the thinning display.
    Airborne Laserscanning (ALS) is used in many regions of the world to record and map the properties of forests. In particular, the tree heights and the density of the trees can be determined very precisely in this way. As an alternative or in addition to this remote sensing method, laser scanning is also used from a tripod or from vehicles to determine the properties of trees.
    In this case, the tree positions and diameter of the trunks can be determined very precisely, including the changes in the diameter including the tree heights. This way it is possible to put standing trees in / 20
    Disassemble logs and their sales value before any
    Estimating timber harvest.
    Laser scanning from an airplane, is called airborne laser scanning (ALS) or laser scanning from a tripod, is called terrestrial laser scanning (TLS), TLS also provides tree coordinates and diameters at different heights.
    Laser scanners are increasingly being used by the automotive industry to support autonomous driving and to recognize other road users in high temporal and spatial resolution and to derive their approach speeds and directions. As a result, smaller, more powerful and more cost-effective laser scanners are coming onto the market. In Scandinavia, successful attempts have already been completed to equip tree harvesters (harvesters) with laser scanners (= harvester laser scanning, HLS) and thus determine the position and diameter information of the surrounding trees similar to the use of TLS. In the case of a thinning, in which usually only 20-40% of the trees in a forest stand are felled, the state of the forest before and after the thinning intervention can be documented with the help of such data.
    Thinnings (DF) are usually care interventions that direct the growth potential to trees with the highest expected increase in value, the greatest productivity (for example due to the size of the tree top) and the best quality. Basically, the trees compete for water, nutrients and light. A DF intervenes in this competitive situation, leading to high value growth, stable stocks and early harvest maturity.
    A forest stand that is optimally thinned several times before it is harvested reaches its maturity (e.g. target diameter) earlier, provides a higher wood quality and is usually more resistant to wind throws and snow breaks. This is of great importance for achieving a financial return in forestry, because these selective interventions can significantly increase productivity in a limited area.
    The classification of the trees in a forest stand into those that are considered eligible due to their properties and those that are part of a / 20
    Thinning as unwanted competitors should be dropped prematurely is referred to as “display. It requires experience and forestry training, and is also time-consuming due to the tedious movement in the dense forest. Ultimately, the person who runs the display must quickly decide which trees are expected to have the greatest increase in value in the coming decades, and which trees are to be removed as "distressers."
    For cost reasons, this value-determining activity is left to the harvester driver, who does not have the necessary overview of his position in the control cabin and therefore makes a suboptimal selection.
    The invention is intended to remedy this deficiency and relates to an expert system based on sensors which is integrated in the harvester and which, during the harvest, takes over the decision in real time based on rules. The growth forecast for each tree in the forest, including its expected response to a thinning intervention, is derived.
    The simulation and prediction of the growth of the individual trees in a forest has been the subject of scientific research for several decades. There are forest growth models (e.g. MOSES, developed at the University of Natural Resources and Applied Life Sciences) that could previously only be used for research purposes because the large-scale availability of the required input parameters (e.g. tree positions, tree heights, size of the tree tops, nutrient and water supply), from which the competitive situation could be assessed, was not previously given. These and many other parameters will be available in the future through Harvester-based Laser Scanning (HLS).
    The processing of HLS data including the processing of information from additional sensors in real time is new.
    In special versions, the laser scanning data is supplemented by digital photos of the trunks and image recognition algorithms coupled to the digital camera, which are used to automatically derive the tree species from the bark pattern and the bark color.
    The tree species is identified by image recognition from the bark pattern and color. Existing geodata for the description of soil properties, geology, / 20
    Precipitation, temperature, solar radiation are taken into account. Tree ring widths are measured on the felled trees in order to obtain information about their previous growth. In addition, soil samples are taken and analyzed. These are taken from different depths and analyzed. The results of the measurements of the recording module are taken into account for the calculation and the growth modeling. Because of the abundance of input data, the data is processed using neural networks (deep learning algorithms).
    Not only are existing forest growth models with the existing data to be obtained during the harvesting process applied in real time, but these models are either expanded with the help of the wealth of data or regenerated using neural networks (deep learning algorithms).
    In any case, it is advantageous to continuously calibrate the forest growth forecast models during the harvesting process using the data obtained.
    In order to determine the growth of a tree over the past few years, the width of its tree rings can be analyzed, from which volume growth is derived. For this purpose, the cut surface that is created when a trunk is cut into individual pieces of wood (bloche) is photographed and the tree ring width on the images is evaluated. This process can already be carried out by automatic analysis of the image.
    A possible additional device according to the invention for an expert system includes a camera that photographs the cut surface immediately after a separating cut and subjects the image to an analysis of the tree ring widths.
    The sensor data described above are processed in real time and the device calibrates itself continuously during the selective harvesting of wood, so that in forest development after thinning intervention the optimum value increase, the highest biodiversity as well as the best stability and risk minimization against forest damage is guaranteed. As an additional result, after the thinning intervention, the selective timber harvesting, detailed single tree data is available across the board.
    / 20th
    If necessary, the control of the device intervenes during the implementation of the
    Procedure for his calculations also on climate and / or tree ring databases.
    Laser scanning data from the harvester can be evaluated according to further criteria. One possibility is the interpretation of the terrain relief. Nutrients and water collect in hollows and quickly evaporate from crests or steep slopes. It can therefore be predicted to a certain extent whether a single tree grows in a more favored or less favored location compared to the trees in its immediate vicinity. This information can be compared with the tree ring widths when cutting and can be used in the display decision algorithms.
    The extent of the tree tops and the mutual shading of the tree tops at different positions of the sun can also be estimated using the laser scanning data and compared with the tree ring widths.
    This means that important input variables are available for assessing the competitive situation of each individual tree with its neighboring trees. They can be compared with the growth derived from the tree ring widths and used for the ongoing calibration of the growth forecast models.
    The growth forecast models in turn serve to optimize the display of the logs to be felled in real time in such a way that the future growth in the value of the forest is maximized.
    A useful additional device for such precision forestry applications can additionally include a device which takes soil samples at different depths of the forest soil by means of drilling probes, as a result of which the nutrient supply to the mineral soil can be analyzed over a small area.
    The interplay of the stored geodata, the accumulated knowledge that has arisen from the comparison of previous sensor data and the current measured values of the sensors enable the maximum yield and / or a minimized / 20 if the corresponding computing power is available
    Failure risk optimized harvesting decision for the individual tree. After harvesting, the system also provides a data set that contains all remaining trees and their growth forecast and is available to forest management for further management issues.
    / 20th
    权利要求:
    Claims (32)
    [1]
    P A T E N T A N S P R Ü C H E
    1. A method for selective wood harvesting and for selective treatment in a forest with a device (1) for felling and / or pruning, which a vehicle (F), as well as a recording module (3) and a controller (4), which access data can, comprises and the vehicle (F) is moved through the forest stock and selectively falls and branches, and the recording module (3) detects the stock to support the navigation of the vehicle (F), characterized in that the recording module (3) at least one decision parameter is recorded and the controller (4) thus makes a decision in real time for selective felling and pruning.
    [2]
    2. The method according to claim 1, characterized in that at least one thickness of a tree is recorded as a decision parameter by the recording module (3).
    [3]
    3. The method according to claim 1 or 2, characterized in that at least one height H of a tree is recorded as a decision parameter by the recording module (3).
    [4]
    4. The method according to any one of claims 1 to 3, characterized in that at least one size G of a crown of a tree is recorded as a decision parameter by the recording module (3).
    [5]
    5. The method according to any one of claims 1 to 4, characterized in that the relative position and / or the distance of a tree to neighboring trees is recorded as a decision parameter by the recording module (3).
    [6]
    6. The method according to any one of claims 1 to 5, characterized in that at least the tree type is automatically determined as a decision parameter from recordings of the recording module (3) by the controller (4).
    [7]
    7. The method according to any one of claims 1 to 6, characterized in that the receiving module (3) has a unit for tree ring detection (J), and tree ring number and / or tree ring thicknesses of the individual trees of the
    15/20
    Forest stand can be measured by this unit of the recording module (3) - preferably during the felling.
    [8]
    8. The method according to any one of claims 1 to 7, characterized in that the recording module has a unit for taking and analyzing soil samples.
    [9]
    9. The method according to any one of claims 1 to 8, characterized in that when processing the recordings by the recording module stored data from previous recordings - preferably by the device performed recordings - of the forest stand are used.
    [10]
    10. The method according to any one of claims 1 to 9, characterized in that precipitation decisions and / or treatment decisions are made in the controller (4) on the vehicle (F).
    [11]
    11. The method according to any one of claims 1 to 9, characterized in that precipitation decisions and / or treatment decisions are made in a controller (4) outside the vehicle (4) and the vehicle (F) is connected to the controller for signal transmission.
    [12]
    12. The method according to any one of claims 1 to 9, characterized in that the decision to make a precipitation is optimized for a given time horizon in terms of economy and / or in terms of stability to wind throw.
    [13]
    13. The method according to any one of claims 1 to 12, characterized in that the geometry and / or the position of tree surfaces are recorded in high resolution by a laser module of the recording module (3).
    [14]
    14. The method according to any one of claims 1 to 13, characterized in that the vehicle navigates with the aid of a global navigation satellite system (GNSS) and determines its position.
    [15]
    15. The method according to any one of claims 1 to 14, characterized in that with at least one camera of the recording module (3) - preferably
    [16]
    16/20 while driving - and / or with a stereo camera, in several directions
    - preferably in any orientation - images of the forest stand are recorded.
    16. The method according to claim 15, characterized in that at least one laser projector projects light spots onto the surroundings during the photo recordings and an image matching method is supported by the light spots in the recordings.
    [17]
    17. The method according to claim 15 or 16, characterized in that the geometry and position of the tree surfaces and the appearance of the tree surface are evaluated stereo-photogrammetrically from the images.
    [18]
    18. The method according to any one of claims 1 to 17, characterized in that the geometry and / or the position of the tree surfaces of the forest stand are recorded with a radar module of the recording module (3).
    [19]
    19. The method according to any one of claims 1 to 18, characterized in that the geometry and / or the position of the tree surfaces of the forest stand are recorded with an ultrasound module of the recording module (3).
    [20]
    20. The method according to any one of claims 1 to 19, characterized in that tree types and / or tree vitality and / or tree damage are automatically detected with the aid of a hyperspectral sensor of the recording module (3).
    [21]
    21. The method according to any one of claims 1 to 20, characterized in that a forest growth model is improved by the recorded data - preferably by tree ring detection, this being particularly preferably carried out in real time.
    [22]
    22. Device (1) for the selective felling and selective treatment of trees, the device (1) comprising a vehicle (F) and comprising at least one recording module (3) and a controller (4) and the device (1) for carrying it out of the method according to one of claims 1 to 21 is suitable, characterized in that the controller (4) is connected to the recording module (3) and the vehicle (F) in a signal-conducting manner and
    17/20 has computing power so that a decision to make a decision is preferably made in real time using at least one model.
    [23]
    23. The device (1) according to claim 22, characterized in that the receiving module (3) has a unit for tree ring detection (J).
    [24]
    24. The device (1) according to claim 23, characterized in that the unit for tree ring detection (J) has at least one sawing tool and at least one optical pickup.
    [25]
    25. The device (1) according to any one of claims 22 to 24, characterized in that the recording module (3) has a laser module for determining the geometry and / or the position of the tree surfaces in high resolution.
    [26]
    26. Device (1) one of claims 22 to 25, characterized in that the vehicle has a global navigation satellite system (GNSS).
    [27]
    27. The device (1) according to one of claims 22 to 26, characterized in that the recording module (3) comprises at least one camera, preferably a digital camera, and the camera is preferably equipped with at least one laser projector, the camera being particularly preferably a stereo camera ,
    [28]
    28. The device (1) according to any one of claims 22 to 27, characterized in that the receiving module (3) has a radar module.
    [29]
    29. The device (1) according to any one of claims 22 to 28, characterized in that the recording module (3) has an ultrasound module.
    [30]
    30. The device (1) according to any one of claims 22 to 29, characterized in that the recording module (3) has a hyperspectral sensor for the visual and automatic detection of tree species, tree vitality or tree damage.
    18/20
    [31]
    31. The device (1) according to any one of claims 22 to 30, characterized in that the controller (4) is arranged on the vehicle.
    [32]
    32. Device (1) according to one of claims 22 to 31, characterized in that the controller (4) is arranged outside the vehicle (F) and that the vehicle (F) is connected to the controller (4) for signal transmission, so that a precipitation decision can be made in real time and can be transmitted to the vehicle (F).
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    同族专利:
    公开号 | 公开日
    AT520253A3|2019-04-15|
    WO2020014719A1|2020-01-23|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    DE102020209864A1|2020-08-05|2022-02-10|Robert Bosch Gesellschaft mit beschränkter Haftung|Method and device for operating a tree felling device and tree felling device|US5894323A|1996-03-22|1999-04-13|Tasc, Inc,|Airborne imaging system using global positioning system and inertial measurement unit data|
    SE9603880D0|1996-10-23|1996-10-23|Bengt Soervik|Forest planning and process|
    SE520298C2|2000-08-15|2003-06-24|Bengt Soervik|Process and aggregates for logging of forest and forest management systems|
    US7218975B2|2004-11-09|2007-05-15|Coe Newnes/Mcgehee Inc.|Integrated mill|
    FI122885B|2005-05-30|2012-08-31|John Deere Forestry Oy|System for measuring the efficiency of a forest machine|
    US9235334B2|2008-05-09|2016-01-12|Genesis Industries, Llc|Managing landbases and machine operations performed thereon|
    FI20090447A|2009-11-26|2011-05-27|Ponsse Oyj|Method and device in connection with a forestry machine|
    US9149010B2|2013-10-31|2015-10-06|Elwha Llc|Harvesting and grafting of trees|
    WO2016075641A1|2014-11-12|2016-05-19|Fibre Gen Holdings Limited|Evaluating trees and tree stems and/or logs|
    ES2762607T3|2015-09-14|2020-05-25|Deere & Co|Method and arrangement to monitor the collection of plant material|
    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    ATA50607/2018A|AT520253A3|2018-07-16|2018-07-16|Selective harvesting method|ATA50607/2018A| AT520253A3|2018-07-16|2018-07-16|Selective harvesting method|
    PCT/AT2019/060234| WO2020014719A1|2018-07-16|2019-07-15|Apparatus and method for selectively harvesting timber|
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