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Tree Detection and Species Identification Using LiDAR Data

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Amin Alizadeh Khameneh

on 13 October 2016

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Transcript of Tree Detection and Species Identification Using LiDAR Data

Tree Detection and Species Identification Using LiDAR Data
A BRIEF REVIEW OF A MASTER THESIS
AMIN ALIZADEH
Dividing an area to several strips with a proper width
Performing outer surface points extraction on projected strip to X-Z plane
Analysing the fitted curve and computing the local maxima points
Clustering Methods
Kmeans
Supervised Kmeans
Hierarchical
Importance of forest inventory
Approx. 60% productive forest land in Sweden
Motivation
Norway spruce 40% Scots pine 38% birch 12%
An alternative or complementary solution for Visimind
Workflow
Workflow
Species identification by fuzzy logic system
Applying different methods on every single tree:
1. Best fit shapes
2. Convex hull
3. Slope changes
4. Point density
Results
Developments in Visimind
Direction vector of each tree
Falling branches
Result
PhD work
The Kriging interpolation method was used for estimating the diameter of the trees for each type of species
Dangerous Trees
the angle between direction vector of each tree and vertical line
The trunk diameter
Species
considering:
The inclination line is computed based on minimised perpendicular distances from all detected points
The orange lines show the distance of each point to centre of mass
Exporting the collisions within 15 degrees
Detecting the nearest tree to the mass centre of each collision.
Applying some conditions to realizing the dangerous collision as a falling branch:
amount of collision mass > 150 points
angle of direction vector > 50 degree

Workflow:
Computing the direction vector of each collision.
Deformation Network Design of Man-Made Construction
THANK YOU FOR YOUR ATTENTION
Dangerous Trees
Examples for single dangerous trees
Full transcript