FI20186029A1

FI20186029A1 - Method and system for generating forestry data

Info

Publication number: FI20186029A1
Application number: FI20186029A
Authority: FI
Inventors: Juho Uusi-Luomalahti; Simo Gröhn; Gustaf Lönn
Original assignee: Prefor Oy
Priority date: 2018-11-30
Filing date: 2018-11-30
Publication date: 2020-05-31
Also published as: WO2020109666A1

Abstract

Disclosed is a method for generating forestry data of a forest (204) having a plurality of trees (304, 306, 308, 310). The method comprises defining a route (202) within the forest (204) for repetitively collecting data as data points using at least one LiDAR (106, 302), repetitively assigning a unique swipe identifier to the data points, and repetitively storing the data points and the swipe identifier to a point cloud database, processing the point cloud database for creating a three-dimensional model of the forest, processing the three-dimensional model to identify a number of trees, assigning and storing a unique tree identifier for each tree in a tree database, processing the three-dimensional model and the assigned tree identifiers for segmenting data points having a given tree identifier for a trunk of the tree, generating a representation of a trunk of the tree, and calculating parameters for the tree from the representation to form the forestry data.

Description

METHOD AND SYSTEM FOR GENERATING FORESTRY DATA

TECHNICAL FIELD

The present disclosure relates generally to forestry; and more specifically, to methods and systems for generating forestry data of a forest having a plurality of trees.

BACKGROUND

Generally, forestry data is a record of information related to a forest. For example, forestry data can include information related to number of trees in the forest, species, health and growth conditions of the trees, pigment compositions and non-pigment constituents in the trees. Furthermore, the data can also include information related to terrain, geology, hydrology of the forest, resource planning, ground surface mapping of the forest area, and so forth.

Conventionally, a LIDAR sensor is employed along a route within the forest area to collect forestry data. A typically LiDAR sensor is capable of acquiring sensory data during both day and night, and seamlessly process the acquired data. 0 " " " " a Typically, the collection of forestry data using the LIDAR sensor comes < 20 with a few disadvantages. The LIDAR sensor locates a large number of o collected datapoints in a point cloud database, therefore it may be difficult 0

I to process such a large number of datapoints in the point cloud database. > Moreover, the accurate positioning/locating of the collected datapoints in

S the point cloud database may be difficult due to a processing of the large 00 5 25 amount of data. Furthermore, the forestry data collected may not be

N highly accurate as the LIDAR sensor manoeuvres continuously on the route. Therefore, formation of the 3D model of a forest may not be precise, thereby creating an inaccurate forestry data.

Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with methods of collecting the forestry data.

SUMMARY

The present disclosure seeks to provide a method for generating forestry data of a forest having a plurality of trees. The present disclosure also seeks to provide a system for generating forestry data of a forest having a plurality of trees. The present disclosure seeks to provide a solution to the existing problem of obtainment of inaccurate forestry data. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in prior art, and provides method and system for generating accurate forestry data of the forest.

In one aspect, an embodiment of the present disclosure provides a method for generating forestry data of a forest having a plurality of trees, the method comprising (i) defining a route within the forest for collecting data; (ii) collecting data as data points using at least one LIDAR guided along © 20 the route, wherein the data is collected during a single swipe of the at > least one LIDAR; = (iii) assigning a unigue swipe identifier to the data points collected during & the single swipe of the at least one LiDAR, storing the data points and the

E swipe identifier to a point cloud database;

ER 25 (iv) repeating steps (ii) and (iii); 3 (v) processing the point cloud database using Simultaneous Localization 2 and Mapping algorithm for creating a three-dimensional model of the forest;

(vi) processing the three-dimensional model of the forest to identify a number of trees, assigning a unique tree identifier to each tree of the number of trees, and storing the tree identifier to a tree database; (vii) processing the three-dimensional model of the forest and the assigned tree identifiers, for segmenting data points having a given tree identifier as belonging to a trunk of the tree; (viii) generating a representation of a trunk of at least one tree, by: (a) using the data associated with said tree, based on the tree identifier; (b) selecting a data set having a given swipe identifier and fitting a circle to said data set, wherein a diameter of the circle corresponds to a diameter of the at least one tree trunk; (c) repeating step (b) to generate a plurality of circles corresponding to a plurality of portions of the at least one tree trunk; and (d) generating a representation of said tree trunk using the plurality of circles; and (ix) calculating parameters for the at least one tree from the representation of the at least one tree to form the forestry data.

In another aspect, an embodiment of the present disclosure provides a system for generating forestry data of a forest having a plurality of trees, © the system comprising:

N - a device comprising a data processing arrangement and at least one 7 LiDAR, wherein the device is configured to: 2 25 (i) collect data as data points using at least one LiDAR guided along

E a route, wherein the data is collected during a single swipe of the

N at least one LIDAR; e (ii) assign a unigue swipe identifier to the data points collected

N during the single swipe of the at least one LIDAR, storing the data points and the swipe identifier to a point cloud database; and (iii) repeat steps (i) and (ii);

a server arrangement communicatively coupled to the data processing arrangement of the device, wherein the server arrangement is configured to: - define the route within the forest for collecting data; - process the point cloud database using Simultaneous Localization and Mapping algorithm for creating a three-dimensional model of the forest; - process the three-dimensional model of the forest to identify a number of trees, assigning a unique tree identifier to each tree of the number of trees, and storing the tree identifier to a tree database; - process the three-dimensional model of the forest and the assigned tree identifiers, for segmenting data points having a given tree identifier as belonging to a trunk of the tree; - generate a representation of a trunk of at least one tree, by: (a) using the data associated with said tree, based on the tree identifier; (b) selecting a data set having a given swipe identifier and fitting a circle to said data set, wherein a diameter of the circle corresponds to a diameter of the at least one tree trunk; (c) repeating step (b) to generate a plurality of circles 2 corresponding to a plurality of portions of the at least = one tree trunk; and

S 25 (d) generating a representation of said tree trunk using the

Ek plurality of circles; and > - calculate parameters for the at least one tree from the 3 representation of the at least one tree to form the forestry data; > and a database arrangement communicatively coupled to each of the device and the server arrangement, wherein the database arrangement comprises the point cloud database and the tree database.

Embodiments of the present disclosure substantially eliminate or at least partially address the aforementioned problems in the prior art, and enable achievement of a fast operating speed, facilitating a fast acquisition and a processing speed. Furthermore, the method and the 5 system enable collection of an accurate forestry data.

Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow.

It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific © 20 methods and instrumentalities disclosed herein. Moreover, those skilled > in the art will understand that the drawings are not to scale. Wherever = possible, like elements have been indicated by identical numbers. a r Embodiments of the present disclosure will now be described, by way of : example only, with reference to the following diagrams wherein:

S 25 FIG. 1 is a block diagram of a system for generating forestry data of a = forest, in accordance with an embodiment of the present s disclosure;

FIG. 2 is a schematic illustration of a route defined within the forest for collecting data, in accordance with an embodiment of the present disclosure;

FIG. 3 is a schematic illustration of a LIDAR in operation for collecting data points, in accordance with an embodiment of the present disclosure;

FIG. 4 is a graphical representation of the data points collected by the

LIDAR, of FIG. 3, to form arcs, in accordance with an embodiment of the present disclosure;

FIG. 5 is a graphical representation of arcs, of FIG.4, processed for generating forestry data, in accordance with an embodiment of the present disclosure; and

FIGs. 6A-B is an illustration of steps of a method for generating forestry data of a forest having a plurality of trees, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a = general item at which the arrow is pointing. ' = DETAILED DESCRIPTION OF EMBODIMENTS a - The following detailed description illustrates embodiments of the present , 25 disclosure and ways in which they can be implemented. Although some

N modes of carrying out the present disclosure have been disclosed, those 3 skilled in the art would recognize that other embodiments for carrying

N out or practising the present disclosure are also possible.

In one aspect, an embodiment of the present disclosure provides a method for generating forestry data of a forest having a plurality of trees, the method comprising (i) defining a route within the forest for collecting data; (ii) collecting data as data points using at least one LIDAR guided along the route, wherein the data is collected during a single swipe of the at least one LiDAR; (iii) assigning a unigue swipe identifier to the data points collected during the single swipe of the at least one LiDAR, storing the data points and the swipe identifier to a point cloud database; (iv) repeating steps (ii) and (iii); (v) processing the point cloud database using Simultaneous Localization and Mapping algorithm for creating a three-dimensional model of the forest; (vi) processing the three-dimensional model of the forest to identify a number of trees, assigning a unigue tree identifier to each tree of the number of trees, and storing the tree identifier to a tree database; (vii) processing the three-dimensional model of the forest and the assigned tree identifiers, for segmenting data points having a given tree identifier as belonging to a trunk of the tree; (viii) generating a three-dimensional model of a trunk of at least one tree, by: 00 > (a) using the data associated with said tree, based on the tree = identifier;

S 25 (b) selecting a data set having a given swipe identifier and fitting a

E circle to said data set, wherein a diameter of the circle corresponds > to a diameter of the at least one tree trunk;

E (c) repeating step (b) to generate a plurality of circles > corresponding to a plurality of portions of the at least one tree trunk; and

(d) generating a representation of said tree trunk using the plurality of circles; and (ix) calculating parameters for the at least one tree from the representation of the at least one tree to form the forestry data.

In another aspect, an embodiment of the present disclosure provides a system for generating forestry data of a forest having a plurality of trees, the system comprising: - a device comprising a data processing arrangement and at least one

LiDAR, wherein the device is configured to: (i) collect data as data points using at least one LiDAR guided along a route, wherein the data is collected during a single swipe of the at least one LiDAR; (ii) assign a unigue swipe identifier to the data points collected during the single swipe of the at least one LIDAR, storing the data points and the swipe identifier to a point cloud database; and (iii) repeat steps (i) and (ii); a server arrangement communicatively coupled to the data processing arrangement of the device, wherein the server arrangement is configured to: - define the route within the forest for collecting data; - process the point cloud database using Simultaneous Localization © and Mapping algorithm for creating a three-dimensional model of

N the forest; 7 - process the three-dimensional model of the forest to identify a 2 25 number of trees, assigning a unigue tree identifier to each tree of

E the number of trees, and storing the tree identifier to a tree : database; © - process the three-dimensional model of the forest and the

N assigned tree identifiers, for segmenting data points having a given tree identifier as belonging to a trunk of the tree; - generate a representation of a trunk of at least one tree, by:

(a) using the data associated with said tree, based on the tree identifier; (b) selecting a data set having a given swipe identifier and fitting a circle to said data set, wherein a diameter of the circle corresponds to a diameter of the at least one tree trunk; (c) repeating step (b) to generate a plurality of circles corresponding to a plurality of portions of the at least one tree trunk; and (d) generating a representation of said tree trunk using the plurality of circles; and - calculate parameters for the at least one tree from the representation of the at least one tree to form the forestry data; and a database arrangement communicatively coupled to each of the device and the server arrangement, wherein the database arrangement comprises the point cloud database and the tree database.

The present disclosure provides the method for generating forestry data of the forest having the plurality of trees. Furthermore, the present disclosure provides the system for generating forestry data of the forest having the plurality of trees. The method comprises associating a swipe identifier with each swipe of the LIDAR that enables generation of an © accurate three-dimensional model of the forest for gathering the forestry

N data. Moreover, the LIDAR employed in the system has a fast operating 7 speed, facilitating a fast acquisition and a processing speed. Therefore, 2 25 the three-dimensional model is processed at a fast rate with a great

E accuracy. It will be appreciated that the point cloud database comprise

N large amount of data points associated with the forest, therefore, e employing the SLAM algorithm in the point cloud database enables

N accurate positioning/locating of the collected data points in the point cloud database, thereby generation of a precise and a guickly processed three-dimensional model of the forest is achieved for gathering the forestry data.

A method for generating the forestry data of the forest having the plurality of trees, wherein the method comprises defining the route within the forest for collecting the data. The forest is generally a vast area comprising a plurality of trees of various species. Therefore, for the generation of forestry data, a record of all the trees and the data associated with the trees is required. A boundary of the forest area for which the forestry area is required is defined. Moreover, the route within the forest for generating the forestry data is defined. The route is such defined that each of the plurality of trees within the boundary of the forest area is covered. Additionally, the defined route has a sufficient number of crossings to ensure each of the plurality of trees in the forest is covered within the route.

Furthermore, the method comprises collecting the data as the data points using the at least one LiDAR guided along the route, wherein the data is collected during the single swipe of the at least one LIDAR. The data associated with the forest is collected along the defined route by using at least one LIDAR guided along the route. The at least one LIDAR is employed to collect the forest data aerially above a ground level of the © forest. Moreover, for the forest areas comprising dense trees the LIDAR > is guided to collect the forest data along the route on the ground of the = forest such that the LIDAR collects the data from the ground level of the

S forest area. In an example, the LIDAR is guided along the route manually

E 25 by a user. In such instance, the user may be a person operating and/or 2 carrying the LiDAR along the defined route. Moreover, the user may be 3 a person carrying the LIDAR in a vehicle being driven along the defined > route. In another example, the LiDAR automatically maneuvers on the defined route without any human assistance. In such an instance, the

LIDAR may be driven along the defined route through an unmanned aerial vehicle such as drones. Furthermore, the LIDAR is configured to determine a distance between a tree of the plurality of trees in a forest and the LIDAR. It will be appreciated that the LIDAR sense a time taken by at least one ray of light emitted by optical emitters (such as one or more channels) of the LIDAR to reach and reflect back to optical receivers of the LIDAR from a target such as a tree and utilize the sensed time taken by the ray of light to calculate a distance between the targeted tree and the LiDAR sensor (such as the optical emitters and receivers). The data (such as the distance) is collected as the data points using the at least one LIDAR. Furthermore, the data points refer to a data collected in a form of points represented in a space.

Optionally, the LIDAR is implemented as one of: a rotating LiDAR, a solid- state LiDAR. The LIDAR employed can be rotating type LIDAR, wherein the LiDAR rotates 360 degrees to collect the data from each direction of the defined route. The LIDAR is configured to rotate with a speed that can be varied accordingly. The LiDAR rotates multiple times collecting the data of the plurality of trees in the forest. Moreover, the LIDAR employed can be the solid-state LIDAR, wherein the solid-state LiDAR collects the data frame by frame. Throughout the present disclosure, the term "single swipe" refers to a data collected by the rotating LIDAR in the single rotation or a data collected by the solid-state LiDAR in the single © frame. The at least one LiDAR collects the data associated with the single a swipe thereof and stores the collected data as the data points.

S Furthermore, the method comprises assigning the unigue swipe identifier

E 25 to the data points collected during the single swipe of the at least one 2 LiDAR, storing the data points and the swipe identifier to the point cloud 3 database. The data collected in the form of the data points during the

S single swipe of the LIDAR is stored in the point cloud database for the processing thereof. It will be appreciated that the data points that are far away or unreliable are eliminated from the point cloud database.

Furthermore, the point cloud database refers to database that is used for carrying out filtering, segmenting, sampling, and registering on point clouds, and thereby reconstructing a three-dimensional model of an object rapidly (such as a tree or the forest) The data associated with the single swipe of the LIDAR is assigned the unique swipe identifier, such that during later stages of the processing, the data corresponding to each of the swipe identifier is processed individually, thereby improving the accuracy. The swipe identifier assigned is further stored in the point cloud database. The data associated with the unique swipe identifier comprises one or more parameters.

Optionally, the swipe identifier comprises a location of the at least one

LiDAR. As the LIDAR maneuvers on the defined route for collecting the data, namely, the location i.e. the geographical co-ordinates associated with a position of the LIDAR changes. The exact location of the LiDAR (such as a latitude, a longitude and an elevation of the LiDAR from the ground level) on the route helps to plot the data points in the point cloud database accurately. Therefore, the swipe identifier may comprise an information about the exact location of the at least one LIDAR at an instant of the single swipe of the LIDAR on the defined route.

Optionally, the swipe identifier comprises an inclination of the at least © one LIDAR with respect to ground. The LIDAR is configured to incline at > certain angles with respect to the ground in order to collect data points = from an inclined view. The LIDAR is inclined in order to determine heights

S of the plurality of trees of the forest. The swipe identifier may comprise

E 25 an information about the inclination of the at least one LiDAR at an instant

QR of the single swipe of the LiDAR. 3 = Furthermore, the swipe identifier comprises a timestamp of the data

N collection. The swipe identifier comprises information about the time associated with the data being collected by the LiDAR during the single swipe. Notably, the rotating LIDAR comprises a plurality of rotations per second, therefore, the timestamp associated with the single swipe is comprised in the swipe identifier. In an example, a LiDAR is employed to collect a forestry data along a route defined on a ground of a forest, wherein the LIDAR rotates 10 times in one second. The data associated with a first swipe of the LIDAR is assigned an identifier (suppose SID1).

The LIDAR is at a point on the route, wherein the geographical location of the point on the route is 62943'18” N, 26°16'47"” E, 112 metres.

Moreover, a height of the LIDAR from the ground of the forest is 2.2 metres. The LiDAR is inclined at an angle of 30° from the horizontal level.

Furthermore, the first swipe of the LIDAR is recorded at a timestamp of

Tuesday, 01-01-2018 and 12341234.234123 seconds. The generated swipe identifier SID1 thus comprises as a mandatory field the timestamp

Tuesday, 01-01-2018 and 12341234.234123 seconds and as optional fields location as 629043'18” N, 26916'47” E, 112 metres, height of LIDAR as 2.2 metres and inclination of the LiDAR as 30°.

Optionally, the method further comprises capturing an image of the at least one tree and associating the image to the point cloud corresponding to the at least one tree. At least one image of the at least one tree may be captured as a reference to the collected data of the LiDAR. Moreover, the image captured is projected onto the point cloud in order to collect accurate data. Furthermore, distinctive colors may be assigned to the © point cloud data using the projected image on the data points of the point a cloud database.

S Optionally, a camera is configured to capture an image of the at least one

E 25 tree. A camera may be employed along with the LiDAR to capture the at 2 least one or more image. The camera is configured to capture high 3 resolution images along the route, wherein the image comprises at least > one tree.

Optionally, the camera is implemented as: a digital camera, or a hyperspectral camera. It will be appreciated that the hyperspectral camera captures images and stored in a digital memory of the hyperspectral camera. Moreover, the hyperspectral camera collects and processes information from across an electromagnetic spectrum.

Notably, the hyperspectral camera is used in order to locate an object, identify a material, detect processes and so forth.

Furthermore, the method comprises collecting the data as the data points using the at least one LiDAR guided along the route, wherein the data is collected during the single swipe of the at least one LIDAR and assigning the unique swipe identifier to the data points collected during the single swipe of the at least one LIDAR, storing the data points and the swipe identifier to a point cloud database iteratively until the data associated with the each of the plurality of trees of the forest is collected along the defined route and the unique swipe identifier is identified to each of the single swipe of the LiDAR.

Furthermore, the method comprises processing the point cloud database using Simultaneous Localization and Mapping algorithm for creating a three-dimensional model of the forest. The LIDAR is configured to maneuver along the entire defined route to collect data points associated with each of the plurality of trees and store the data points comprising the swipe identifiers in the point cloud database. A Simultaneous © Localization and Mapping (SLAM) algorithm enables simultaneous > estimation of the position of a position recognizing apparatus and a map = of an environment of the position recognizing apparatus by repeating a

S consecutive motion including building a map of the environment of the

E 25 position recognizing apparatus and localizing the position recognizing 2 apparatus, which has moved to a new position, based on the built map. 3 The Simultaneous Localization and Mapping (SLAM) algorithm is applied

S to the data points in the point cloud database to accurately locate and map the data points in the point cloud database, thereby creating the three-dimensional (3D) model of the forest. It will be appreciated that the SLAM algorithm facilitates locating of trunks of each of the plurality of trees on the point cloud database. Moreover, the SLAM algorithm utilizes the LIDAR related information i.e. the information associated with the single swipe of the LIDAR for localization of the plurality of trees.

Optionally, the method further comprises partitioning the data collected as the point cloud into a plurality of cubical sections. The point cloud database may be partitioned into the plurality of cubical sections for computational/processing purposes, wherein each of the plurality of cubical sections comprise a part of the created 3D model of the forest. It will be appreciated that the forest may be a vast area, so the point cloud database of the forest will be immense. Therefore, for precise processing of the point cloud database, the point cloud database is sectioned into the plurality of cubes, such that each of the plurality of cubical sections are processed one at a time. The plurality of cubical sections may or may not be of equal dimensions.

Furthermore, the method comprises processing the three-dimensional model of the forest to identify a number of trees, assigning a unique tree identifier to each tree of the number of trees, and storing the tree identifier to a tree database. The 3D model of the forest partitioned into the plurality of cubical sections is processed section wise. The processing © is performed in order to identify the number of trees in the forest. It will > be appreciated that each of the tree comprises various parts such as = trunk, branches, leaves and so forth. In an example, a first cubical

S section comprises a tree. Moreover, few of the leaves of the tree are in

E 25 the adjacent cubical section. Therefore, for processing purposes, the 2 data points associated with the few of the leaves in the adjacent tiles are 3 allocated in the first cubical section. The unique tree identifier is assigned > to each of the identified tree of the forest. The tree identifier is further stored in the tree database. It will be appreciated that the tree database is an organized body of digital information regardless of the manner in which the data or the organized body thereof is represented.

Furthermore, the method comprises processing the three-dimensional model of the forest and the assigned tree identifiers, for segmenting data points having the given tree identifier as belonging to a trunk of the tree.

Furthermore, the dimensions of the tree are identified by processing the 3D model of the forest. The 3D model of the forest is processed to fetch each of the tree identifiers. Furthermore, the data points associated with each of the tree identifiers are processed to segment the data points, such as to identify the data points belonging to the trunk of the tree.

Furthermore, the method comprises generating a representation of the trunk of at least one tree by using the data associated with said tree, based on the tree identifier. The data points belonging to the trunk of the tree are identified for the generation of the representation of the trunk of the identified tree. It will be appreciated that assignment of the tree identifier to each tree of the number of trees of the forest helps in identifying the data points associated with the trunk of the tree accurately.

Furthermore, the method comprises selecting the data set having the given swipe identifier and fitting a circle to said data set, wherein a x diameter of the circle corresponds to a diameter of the at least one tree a trunk. Furthermore, the data set associated with the each of the swipe 5 identifiers is selected for accurate processing thereof. It will be 2 appreciated that the trunk of the tree is generally conical in shape. , 25 Therefore, for an estimation of a diameter of the trunk, the data set is

N fitted into a normalized circle having the diameter as that of the trunk of 3 the tree. During the maneuvering of the LiDAR along the route, the LIDAR

N produces multiple swipes, wherein each swipe creates the data points in a shape of an arc around the trunk of the tree. The created data points in the shape of the arc are normalized and fitted into the circle having the diameter same as that of the trunk. Notably, when the LIDAR is inclined at the angle with respect to the ground, the arcs that are formed are generally ellipsoid arcs in shape. The ellipsoid arcs are further normalized and fitted into the circle having the diameter same as that of the trunk of the tree.

Furthermore, the method comprises selecting the data set having the given swipe identifier and fitting the circle to said data set iteratively to generate a plurality of circles corresponding to a plurality of portions of the at least one tree trunk. The swipe identifiers corresponding to the trunk of the tree are selected to generate the plurality of circles corresponding to the trunk. The plurality of circles is notably of varying diameters as the trunk is generally conical in shape. The plurality of circles generated correspond to a height from top of the trunk to a bottom of the trunk of the tree. Therefore, the diameter of the entire trunk of the tree is determined.

Furthermore, the method comprises generating a representation of said tree trunk using the plurality of circles. The 3D model of the trunk of the tree is generated by using the plurality of circles corresponding to the trunk, with the assistance of the swipe identifier and the tree identifier associated with the trunk of the tree. It will be appreciated that the 3D © model of the trunk of each of the tree of the forest is generated using the > plurality of circles utilizing the swipe identifiers and the tree identifier of = the respective trunk of the trees. The 3D model of the trunk of the trees

S have the accurate diameters thereof that may be utilized for a study of

E 25 thetreesoftheforest such as growth of the tree for gathering the forestry o data. 2 = Furthermore, the method comprises calculating parameters for the at

N least one tree from the representation of the at least one tree to form the forestry data. The representation of the at least one tree is utilized to calculate at least one parameter related to the at least one tree for gathering the forestry data. It will be appreciated that the method is repeated from time to time for the monitoring of the growth of each of the plurality of trees of the forest. Moreover, the parameters may be related to different parts of the trees such as the trunk, the branch, the leaves and so forth.

Optionally the method further comprises segmenting the representation of the at least one tree into at least one of the tree trunk, branches, or leaves. The representation of the at least one tree may be segmented into the at least one part such as the trunk of the tree, branches and the leaves for the processing thereof. It will be appreciated that the segmenting of the parts of the tree is based on the swipe identifier and the tree identifier. The data points corresponding to each of the plurality of trees is processed to identify the plurality of trees. Moreover, the swipe identifiers corresponding to the segmented parts are fetched to process the parts of the trees. In an example, a representation of a tree is segmented into a trunk, a plurality of branches and the plurality of leaves.

For processing of the plurality of leaves of the tree, the data points associated with the plurality of leaves are identified as the representation is segmented into different parts. Furthermore, the data points corresponding to the plurality of leaves are processed. Optionally, the segmenting of the at least one tree may be performed using various © methods such as a watershed method, a point density map method, a a tree shape recognition method and so forth.

S Furthermore, the method comprises calculating parameters for at least

E 25 one of the tree trunk, branches, or leaves of the at least one tree. As 2 mentioned above, there may be one of more parameters associated with 3 the tree that are relevant for the gathering of the forestry data such as

S growth of the individual trees, a volume of the trunk of the tree, straightness of the trunk of the tree, suitability of the trees for obtainment of timber, photosynthetic process in the trees, an information about the life of the trees, infected trees and so forth. Moreover, the forestry data may comprise information about types of woods of the trees suitable for fuel, natural water quality management, a recreation purpose of the forest area, aesthetically appealing landscape of the forest, protection of the forest, aesthetically appealing landscapes of the forest, biodiversity management in the forest, watershed management of the forest, erosion control, preserving forests as for atmospheric carbon dioxide and so forth. The representation comprising data points associated with the trunk of the tree reveals plenty of information about the tree.

Furthermore, the representation comprising data points associated with the branches of the tree shows the information about a highest branch, a lowest branch, alive branches, dead branches of the tree and so forth.

Moreover, the representation comprising data points associated with the leaves of the tree reveals information about the shape of the leaves, size of the leaves, dead leaves and so forth. Therefore, the representation of the at least tree may be used to calculate the one or more parameters for gathering the forestry data.

Furthermore, the method comprises determining a species of the at least one tree from the parameters. It will be appreciated that the plurality of trees in the forest may be of one or more species. The species of the at least one tree is determined from the calculated parameters for the © gathering of the forestry data, by comparing the said parameters, such

N as density and form of branches, height of a lowest branch and so forth, 7 to commercially available tree models. In an example, the forest 2 25 comprises a plurality of pine trees. Therefore, a species of the pine trees

E such as "Pinus sylvestris" will be determined using the determined : parameters of the pine tree. Optionally, the number of trees © corresponding to each of the determined species in the forest are

N determined.

Furthermore, the method comprises storing the representation and the species of the at least one tree in the tree database. The 3D model of the forest with the information about species of each of the tree of the forest in the 3D model is stored in the tree database. The tree database may be accessed again in order to compare the parameters such as growth of the trees, diseases and so forth with the 3D models of the forest generated later.

The present disclosure also relates to the system as described above.

Various embodiments and variants disclosed above apply mutatis mutandis to the system.

The system includes the device comprising the data processing arrangement and the at least one LIDAR. Furthermore, the system comprises the server arrangement communicatively coupled to the data processing arrangement of the device. Moreover, the system comprises the database arrangement communicatively coupled to each of the device and the server arrangement. The device refers to a unit comprising a housing to accommodate the data processing arrangement and the at least one LIDAR. Throughout the present disclosure, the term ‘data processing arrangement’ as used herein relates to programmable and/or non-programmable components configured to execute one or more © software application for storing, processing and/or share data and/or set > of instruction. Optionally, the data processing unit can include, for = example, a component included within an electronic communications

S network. Additionally, the data processing arrangement include one or

E 25 more data processing facilities for storing, processing and/or share data 2 and/or set of instruction. Furthermore, the data processing arrangement 3 includes hardware, software, firmware or a combination of these, suitable

S for storing and processing various information and services accessed by the one or more user using the one or more device. Optionally, the data processing arrangement include functional components, for example, a processor, a memory, a network adapter and so forth. Throughout the present disclosure, the term ‘server arrangement’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information.

Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks. Furthermore, it should be appreciated that the server may be both single hardware server and/or plurality of hardware servers operating in a parallel or distributed architecture. In an example, the server may include components such as memory, a processor, a network adapter and the like, to store, process and/or share information with other computing components, such as the device/user equipment. Optionally, the server is implemented as a computer program that provides various services (such as database service) to other devices, modules or apparatus. Throughout the present disclosure, the term 'database arrangement' as used herein relates to an organized body of digital information regardless of the manner in which the data or the organized body thereof is represented. Optionally, the database may be hardware, software, firmware and/or any combination thereof. For example, the organized body of related data may be in the form of a table, a map, a grid, a packet, a datagram, a file, a document, a list or in any other form. The database includes any data storage software and 2 systems, such as, for example, a relational database like IBM DB2 and = MySQL. Optionally, the database may be used interchangeably herein as

S 25 database management system, as is common in the art. Furthermore,

Ek the database management system refers to the software program for > creating and managing one or more databases. Optionally, the database

E may be configured to supports relational operations, regardless of > whether it enforces strict adherence to the relational model, as understood by those of ordinary skill in the art. Additionally, the database populated by data elements. Furthermore, the data elements may be data records and/or bits of data, which terms are used interchangeably herein, and are intended to mean information stored in records of a database.

Optionally, the device further comprises a location determination unit, an inertial measurement unit (IMU) and a clock; and wherein the swipe identifier comprises a location of the at least one LIDAR determined using the location measurement unit, an inclination of the at least one LiDAR with respect to ground determined using the inertial measurement unit and a timestamp of the data collection determined using the clock. The location determination unit refers to the unit that utilizes satellites, receivers, algorithms and so forth to measure and determine an exact location. The location determination unit includes, but are not limited to global positioning system (GPS) and global navigation satellite system (GNSS). The IMU refers to one or more electronic devices that measures and determine force of an entity, angular rate of the entity and so forth by employing a combination of sensors such as an accelerometer and so forth.

Optionally, the device further comprises a camera configured to capture the image of the at least one tree; and wherein the server arrangement is further configured to associate the image to the point cloud © corresponding to the at least one tree.

O

= Optionally, the server arrangement is further configured to segment the 5 representation of the at least one tree into at least one of the tree trunk,

O

- branches, or leaves, calculate parameters for at least one of the tree , 25 trunk, branches, or leaves of the at least one tree, determine a species

N of the at least one tree from the parameters and store the representation 3 and the species of the at least one tree in the tree database.

N

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to FIG. 1, there is shown a system 100 for generating forestry data of a forest having a plurality of trees, in accordance with an embodiment of the present disclosure. The system 100 comprises a device 102, wherein the device 102 houses a data processing arrangement 104 and a LIDAR 106. Furthermore, a server arrangement 108 may be communicatively coupled to the data processing arrangement 104 housed within the device 102. Moreover, a database arrangement 110 is communicatively coupled to the server arrangement 108 and the device 102.

Referring to FIG. 2, there is shown a schematic illustration 200 of a route 202 defined within the forest 204 for collecting data, in accordance with an embodiment of the present disclosure. The LIDAR manoeuvres on the route 202 of the forest 204 to collect data points corresponding to a plurality of trees. Moreover, the defined route 202 comprises a plurality of crossings, depicted as a crossing 206, to ensure collection of an accurate data.

Referring to FIG. 3, there is shown a schematic illustration 300 of a LIDAR © 20 302 in operation for collecting data points, in accordance with an > embodiment of the present disclosure. The LiDAR 302 is configured to = collect data points associated with trunks of a plurality of trees 304, 306, o 308 and 310 by rotating axially. Moreover, the data points are collected

E in a single swipe of the LIDAR 302, wherein the data points associated

ER 25 with the single swipe are assigned a unique swipe identification. 3 Furthermore, the LIDAR 302 is projecting a laser light 312 (depicted as

S a ray of light that rotates as the LiDAR rotates) on trunks of the plurality of trees 304, 306, 308 and 310 to collect data points. It will be appreciated that when the LIDAR 302 projects the laser light 312 on the trunks 304, 306, 308 and 310 to collect the data points, the data points are formed in form of an arc as the trunks 304, 306, 308 and 310 are cylindrical in shape.

Referring to FIG. 4, there is shown a graphical representation 400 of the data points collected by the LIDAR 302, of FIG. 3, to form arcs 404-410, in accordance with an embodiment of the present disclosure. The arc 404 corresponds to the trunk of the tree 304, the arc 406 corresponds to the trunk of the tree 306, the arc 408 corresponds to the trunk of the tree 308 and the arc 410 corresponds to the trunk of the tree 310.

Moreover, the arcs 404-410 as depicted are formed by the single swipe of the LIDAR 302. The LIDAR 302 is configured to rotate 360° for collecting data points. The formation of data points as the arcs 404-410 at different LIDAR angles as the LIDAR 302 rotates axially, is shown on a horizontal axis. Furthermore, formation of the data points as the arcs 404-410 at distances with respect to the LIDAR 302 is shown on a vertical axis. It will be appreciated that the arc 410 corresponding to the trunk of the tree 310 of the FIG. 3 is unclear and does not form a clear arc 410, therefore, the data points of the trunk of the tree 310 are discarded.

Referring to FIG. 5, there is shown a graphical representation 500 of arcs © 402-406, of FIG. 4, processed for generating forestry data, in > accordance with an embodiment of the present disclosure. The arcs 404, = 406 and 408 formed in the FIG. 4 are fitted into a plurality of circles

S 504, 506 and 508 respectively. The arc 404 corresponding to the trunk

E 25 of the tree 304 (of FIG.3) is fitted into a circle 504, the arc 406 2 corresponding to the trunk of the tree 306 (of FIG.3) is fitted into a circle 3 506 and the arc 408 corresponding to the trunk of the tree 308 (of

S FIG.3) is fitted into a circle 508. A diameter of the plurality of circles 504, 506 and 508, correspond to a diameter of the respective trunk of the trees 304-308 (of FIG.3).

Referring to FIGs. 6A-B, there is shown an illustration of steps of a method 600 for generating forestry data of a forest having a plurality of trees, in accordance with an embodiment of the present disclosure. At a step 602, a route is defined within the forest for collecting data. At a step 604, the data as data points using at least one LiDAR guided along the route is collected, wherein the data is collected during a single swipe of the at least one LIDAR. At a step 606, a unique swipe identifier is assigned to the data points collected during the single swipe of the at least one LIDAR, storing the data points and the swipe identifier to a point cloud database. At a step 608, the data as the data points are collected using the at least one LiDAR guided along the route, wherein the data is collected during the single swipe of the at least one LIDAR and the unique swipe identifier is assigned to the data points collected during the single swipe of the at least one LIDAR, storing the data points and the swipe identifier to the point cloud database iteratively until the data associated with the each of the plurality of trees of the forest is collected along the defined route and the unique swipe identifier is identified to each of the single swipe of the LIDAR. At a step 610, the point cloud database is processed using Simultaneous Localization and Mapping algorithm for creating a three-dimensional model of the forest. At a step 612, the three-dimensional model of the forest is processed to identify a number of trees, assigning a unique tree identifier to each tree of the number of 2 trees, and storing the tree identifier to a tree database. At a step 614, = the three-dimensional model of the forest and the assigned tree

S 25 identifiers are processed, for segmenting data points having a given tree x identifier as belonging to a trunk of the tree. At a step 616, a o representation of a trunk of at least one tree is generated by using the 3 data associated with said tree, based on the tree identifier, a data set > having a given swipe identifier is selected and a circle is fitted to said data set, wherein a diameter of the circle corresponds to a diameter of the at least one tree trunk, the data set having the given swipe identifier is selected and the circle is fitted to said data set iteratively to generate a plurality of circles corresponding to a plurality of portions of the at least one tree trunk and a representation of said tree trunk is generated using the plurality of circles. At a step 618, parameters for the at least one tree are calculated from the representation of the at least one tree to form the forestry data.

Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, “is” used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural.

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Claims

1. A method for generating forestry data of a forest having a plurality of trees, the method comprising: (i) defining a route within the forest for collecting data; (ii) collecting data as data points using at least one LIDAR guided along the route, wherein the data is collected during a single swipe of the at least one LiDAR; (iii) assigning a unigue swipe identifier to the data points collected during the single swipe of the at least one LiDAR, storing the data points and the swipe identifier to a point cloud database; (iv) repeating steps (ii) and (iii); (v) processing the point cloud database using Simultaneous Localization and Mapping algorithm for creating a three-dimensional model of the forest; (vi) processing the three-dimensional model of the forest to identify a number of trees, assigning a unigue tree identifier to each tree of the number of trees, and storing the tree identifier to a tree database; (vii) processing the three-dimensional model of the forest and the assigned tree identifiers, for segmenting data points having a given tree identifier as belonging to a trunk of the tree; (viii) generating a representation of a trunk of at least one tree, by: © (a) using the data associated with said tree, based on the tree N identifier; 7 (b) selecting a data set having a given swipe identifier and fitting a 2 25 circle to said data set, wherein a diameter of the circle corresponds & to a diameter of the at least one tree trunk; N (c) repeating step (b) to generate a plurality of circles e corresponding to a plurality of portions of the at least one tree N trunk; and (d) generating a representation of said tree trunk using the plurality of circles; and

(ix) calculating parameters for the at least one tree from the representation of the at least one tree to form the forestry data.

2. A method according to claim 1, wherein the swipe identifier comprises: - a location of the at least one LiDAR; - an inclination of the at least one LIDAR with respect to ground; and - a timestamp of the data collection.

3. A method according to any one of the preceding claims, further comprising: - capturing an image of the at least one tree; and - associating the image to the point cloud corresponding to the at least one tree.

4. A method according to any one of the preceding claims, further comprising: - segmenting the representation of the at least one tree into at least one of the tree trunk, branches, or leaves; - calculating parameters for at least one of the tree trunk, branches, or leaves of the at least one tree; - determining a species of the at least one tree from the parameters; and - storing the representation and the species of the at least one tree in the 2 tree database. &

=

5. A method according to any one of the preceding claims, further & comprising partitioning the data collected as the point cloud into a E plurality of cubical sections. O S 25

6. A system for generating forestry data of a forest having a plurality of = trees, the system comprising: N - a device comprising a data processing arrangement and at least one LiDAR, wherein the device is configured to:

(i) collect data as data points using at least one LiDAR guided along a route, wherein the data is collected during a single swipe of the at least one LiDAR; (ii) assign a unigue swipe identifier to the data points collected during the single swipe of the at least one LIDAR, storing the data points and the swipe identifier to a point cloud database; and (iii) repeat steps (i) and (ii);

a server arrangement communicatively coupled to the data processing arrangement of the device, wherein the server arrangement is configured to:

- define the route within the forest for collecting data;

- process the point cloud database using Simultaneous Localization and Mapping algorithm for creating a three-dimensional model of the forest;

- process the three-dimensional model of the forest to identify a number of trees, assigning a unigue tree identifier to each tree of the number of trees, and storing the tree identifier to a tree database;

- process the three-dimensional model of the forest and the assigned tree identifiers, for segmenting data points having a given tree identifier as belonging to a trunk of the tree;

- generate a representation of a trunk of at least one tree, by:

2 (a) using the data associated with said tree, based on the = tree identifier;

S 25 (b) selecting a data set having a given swipe identifier and E fitting a circle to said data set, wherein a diameter of the > circle corresponds to a diameter of the at least one tree E trunk;

> (c) repeating step (b) to generate a plurality of circles corresponding to a plurality of portions of the at least one tree trunk; and

(d) generating a representation of said tree trunk using the plurality of circles; and - calculate parameters for the at least one tree from the representation of the at least one tree to form the forestry data; and a database arrangement communicatively coupled to each of the device and the server arrangement, wherein the database arrangement comprises the point cloud database and the tree database.

7. A system according to claim 6, wherein the LiDAR is implemented as one of: a rotating LIDAR, a solid-state LiDAR.

8. A system according to claim 7, wherein the device further comprises a location determination unit, an inertial measurement unit (IMU) and a clock; and wherein the swipe identifier comprises: - a location of the at least one LIDAR determined using the location measurement unit; - an inclination of the at least one LIDAR with respect to ground determined using the inertial measurement unit; and - a timestamp of the data collection determined using the clock.

9. A system according to any one of the claims 6-8, wherein the device further comprises a camera configured to capturing an image of the at least one tree; and wherein the server arrangement is further configured = to associate the image to the point cloud corresponding to the at least = one tree. &

10. A system according to claim 9, wherein the camera is implemented E as: a digital camera, or a hyperspectral camera. O S 25

11. A system according to any one of the claims 6-10, wherein the server = arrangement is further configured to: N - segment the representation of the at least one tree into at least one of the tree trunk, branches, or leaves;

- calculate parameters for at least one of the tree trunk, branches, or leaves of the at least one tree; - determine a species of the at least one tree from the parameters; and - store the representation and the species of the at least one tree in the tree database.

12. A system according to any one of the claims 6-11, wherein the server arrangement is further configured to partition the data collected as the point cloud into a plurality of cubical sections.

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