application of a pattern recognition algorithm for single tree detection from lidar data

APPLICATION OF A PATTERN RECOGNITION ALGORITHM FOR SINGLE TREE DETECTION FROM

LiDAR DATA

FOSS4G-EU 2017 – 2017/07/21

A. Antonello, S. Franceschi, V. Floreancig, F. Comiti(*), G. Tonon(*)

(*) Free University of Bolzano

INDEX

1. Research objective

2. Tools to extract vegetation from LiDAR data

3. Application on the study area

4. Conclusions

RESEARCH OBJECTIVE

Evaluate the use of LiDAR data to extract forest parameters: ● position and height of single trees ● forest volume.

Extrapolation of the whole forest biometric information (e.g. tree height, DBH, forest biomass) from remote sensing data can be obtained through two approaches:● area-based approaches (AB): forest attributes are

estimated relating plot information to ALS data by statistically procedure

● individual tree crown (ITC) approaches: are aimed to detect position and main characteristics of each single tree. Single-tree records can then be aggregated at plot, forest, watershed or regional scale.

EXTRACT VEGETATION FROM LiDAR

Extrapolation of the whole forest biometric information (e.g. tree height, DBH, forest biomass) from remote sensing data can be obtained through two approaches:● area-based approaches (AB): forest attributes are

estimated relating plot information to ALS data by statistically procedures

● individual tree crown (ITC) approaches: are aimed to detect position and main characteristics of each single tree. Single-tree records can then be aggregated at plot, forest, watershed or regional scale.


EXTRACT VEGETATION FROM LiDAR● three models: Local Maxima

raster and point cloud + pattern recognition raster

● three models: Local Maxima raster and point cloud + pattern recognition raster

● matching procedure between extracted and measured trees is based on relative position (distance) and height




● automatic calibration is based on Particle Swarming Optimizer (PS)




● automatic calibration is based on Particle Swarming Optimizer (PS)

● three fitness functions based on the statistical concept of Information Retrieval: minimize FP, minimize FN and the harmonic mean of FP and FN


● Local Maxima algorithms identify points with the highest elevation value within a given moving window

RASTER POINT CLOUD

Moving window circular circular

Center of window each raster cell all point of the cloud

Radius constant constant

Data used DSM point cloud + DTM

Calibration params ● maximum radius● height difference for

branches● min tree height

● maximum radius● height difference for

branches


● Pattern Recognition model is based on the geomorphon algorithm (Jasiewicz, 2013)

● uses a pattern based classification of landforms: characterization of the local surface using the line-of-sight principle (LOS)

● LOS describes the unimpeded view or access from one point to another point across a terrain or surface

● LOS is used normally to understand the visible and obstructed (non visible) points in terrain analysis

● 498 possible different patterns of which 10 are the mostly common → 4 investigated for tree detection



Parameters of the Geomorphon model are:● maximum lookup distance: in the 8 main directions● flatness threshold: vertical angle (angle between the horizontal

plane and the line connecting the central cell with the point located on the profile at the line-of-sight distance or at the maximum allowed distance)

● additional checks have been implemented for filtering peaks which are surrounded by:

● no more than 2 not valid cells (otherwise it is considered a point on the boundary of a crown)

● no other peaks

● not a coexistence of pits, valleys, spurs and hollows

● treetops extracted from the geomorphon peaks do not have in their surrounding other sharp morphologies, there should be slopes, ridges or shoulders which indicate a continuity of the shape that generates the central peak.


EXTRACT VEGETATION FROM LiDAR● raster based using as reference surface the CHM

● calibrated parameters:

➢ the maximum lookup distance for the LOS

➢ the flatness threshold (difference between the nadir and the zenith angles) for the LOS

➢ the height difference for filtering branches (filter on breaching patterns of trees)

● output is a set of treetops position with the height as attribute

FALSE POSITIVEextracted by the software but not existing in reality

mapped treesextracted trees

FALSE NEGATIVEexisting but not extracted

by the software

TRUE POSITIVEextracted by the software

and matched with measured

● AGB is calculated using allometric functions and hypsometric relationship where the parameters are extracted from field data

V=a⋅π⋅D2

4⋅H

DLIDAR=b⋅H2+c H+d

STUDY AREA a b c d

Val Aurina 0.0000368048 0.0096 1.298 0.00


STUDY AREAAURINA VALLEY high local variety in forest structure

●Norway spruce (Picea abies)●Larch (Larix decidua)●Stone pine (Pinus cembra)

AREA = 10 km2

LiDAR SURVEY OF 09/2012

points density = 10 p/m2

FIELD SURVEY SUMMER 2013

3 FOREST STRUCTURES:monoplane – biplane – multilayer

12 circular plotsradius = 15 m(position + height + DBH + species)

CHARACTERISTIC Monoplane Biplane Multilayer

Tree density (n/ha) 427.2 473.9 331.0

Basal area (m2/ha) 26.7 39.9 34.6

Average Basal area (m2) 0.064 0.101 0.114

Volume AGB (m3/ha) 121.0 387.4 283.7

STUDY AREA

RESULTS

Simulation have been done using the Particle Swarming automatic calibration. The classification of results considered:● 3 different models

➢ LM raster based➢ LM point cloud based➢ Geomorphon raster based

● 3 different fitness functions➢ minFP➢ minFN➢ harmonic mean of FP and FN

● 3 different forest strutures➢ monoplane➢ biplane➢ multilayer

Mean detection rates of treetops

Rmat=N tp

Nmap

Rcom=N fp

Nmap

Rext=Next

Nmap

RESULTS

METHOD Rext Rmat Rcom Rom

PS_rast 0.95 0.74 0.21 0.26

PS_pc 0.88 0.77 0.11 0.23

PS_geo 1.01 0.80 0.22 0.20

AVERAGE 0.95 0.77 0.18 0.23

Tree height: RMSE

RMSE h=∑√(1−

hexthmeas )

2

N

RESULTS

Volume rates

RESULTS

METHOD Rext Rmat Rcom Rom

PS_rast 1.03 0.80 0.23 0.16

PS_pc 0.99 0.87 0.13 0.13

PS_geo 1.06 0.84 0.22 0.11

AVERAGE 1.03 0.84 0.19 0.13

JGRASSTOOLS LIBRARY

● an open source geospatial library focused on hydro-geomorphological analysis and environmental modeling

● development started in 2002 at the University of Trento, Department of Civil and Environmental Engineering

● in 2012 started the development of LESTO (LiDAR Empowered Scientific Toolbox Open Source) in collaboration with the University of Bolzano

● from 2015 integrated as Spatial Toolbox in gvSIG● available for installation through the gvSIG Update

Manager as a plugin

JGRASSTOOLS LIBRARY

Models are grouped in sections and subsections. Main sections are:

● HortonMachine: geomorphology analysis

● Raster and vector processing● Mobile tools: support for

Geopaparazzi application for digital field mapping

● LESTO: LiDAR analysis (forestry)

JGRASSTOOLS LIBRARY

select the maps and define the parameters

CONCLUSIONS● the use of PSO iterative parametrization compared

with commonly used LM algorithms with manual calibration performs better in terms of detection rates

● the new model based on LM for point cloud (PC) is the one that performs better in all the analyzed conditions and also for different trees species (conifers and broadleaves)

● the new model based on pattern recognition (geomorphon) is a valid alternative to standard LM if only raster based input data are available

● the fitness function based on the harmonic mean of FP and FN seems to be the most accurate for both volume and number of trees

Andrea Antonello, Silvia FranceschiV. Floreancig, F. Comiti, G. Tonon

THANKS FOR THE ATTENTION!