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Tree Detection and Species Identification Using LiDAR Data
Transcript of Tree Detection and Species Identification Using LiDAR Data
A BRIEF REVIEW OF A MASTER THESIS
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
Importance of forest inventory
Approx. 60% productive forest land in Sweden
Norway spruce 40% Scots pine 38% birch 12%
An alternative or complementary solution for Visimind
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
Developments in Visimind
Direction vector of each tree
The Kriging interpolation method was used for estimating the diameter of the trees for each type of species
the angle between direction vector of each tree and vertical line
The trunk diameter
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
Computing the direction vector of each collision.
Deformation Network Design of Man-Made Construction
THANK YOU FOR YOUR ATTENTION
Examples for single dangerous trees