Journal of Environmental Science and Engineering A 5 (2016) 511-515 doi:10.17265/2162-5298/2016.10.003

Tree Stability Evaluation: the Importance of Monitoring

Programs

Alberto Minelli, Cevenini Laura, Corradini Matteo, Pasini Ilaria and Zuffa Daniele

Department of Agricultural Sciences, University of Bologna Alma Mater Studiorum, Bologna 40127, Italy

Abstract: One of the main issues in tree stability evaluations is the scheduling of adequate monitoring programs. Generally, after a tree analysis, an arborist indicates the required maintenance operations and the timing for periodical inspection. Field conditions, tree species and biomechanical defects influence the plan. Three old trees (Populus spp. and Celtis australis) located within Golf Club Verona (Sommacampagna, Italy) were monitored periodically from 2010 to present. In addition to visual assessment, authors used sonic tomography to evaluate development of internal defects and planning the maintenance. The aim of this work is to identify a methodology for observing significant difference in tomograms, in order to understand the appropriate interval between instrumental analyses. Key words: Tree stability evaluation, arboriculture, sonic tomogram.

1. Introduction

During the tree stability evaluation process, the

arborist describes field conditions, collects tree data

and notes biomechanical defects. If required, the use

of technical instrument, such as incremental borer or

sonic tomograph, is recommended. The instrumental

analysis helps the arborist to understand the internal

situation of the tree section. In order to provide a

correct evaluation, the arborist may analyze hazard

and risk to define likelihood of damage [1-5]. Tree

stability evaluation is valid until a future check up,

which is determined by an arborist. The monitoring

program is a fundamental step of a tree analysis. The

arborist can choose between visual or instrumental

recheck, depending case by case. If the instrumental

recheck is selected, the technician can choose between

incremental borer, sonic or electrical tomograph,

pulling test, or another instrumental analysis [1, 5].

This choice depends on biomechanical defect and

previous analysis (repetitiveness).

The aim of this work is to understand the effective

interval between instrumental analysis on trees by

Corresponding author: Alberto Minelli, researcher, main

research field: arboriculture, urban green and green design.

observing graphs and sonic velocities variation.

2. Materials and Methods

From 2010 to present, three trees were periodically

monitored at the Golf Club Verona (Sommacampagna,

Italy). Two poplars and a nettle tree showed extended

wood decay, at the basis of the trunk or at crown.

The analysis was carried out using sonic tomograph

Arbotom® (Rinntech e.K., Heidelberg, Germany).

Using sonic impulses, the tomograph creates an

internal view of trunk section. Wood decay, cavities

and cracks are visible in a colored graph, called

tomogram. The sonic modules are positioned around

the investigated section; an arborist hits a sensor with

a hammer, sound travels through wood reaching all

sensors that record sound velocity. Hitting all sensors,

software creates tomogram that returns a sonic image

of internal wood.

The dimension of trees and period analysis are

shown in Table 1. The graphs are compared in a table

and the data obtained (sonic velocities) are evaluated

with General Linear Model (GLM, Statistica 7.0

Statsoft, Inc.). GLM is a statistical linear model that

incorporates F-test, which compares two variances.

F-test proves the null hypothesis, which is variance

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Tree Stability Evaluation: the Importance of Monitoring Programs

512

Table 1 Dimension of trees and periodanalysis.

Case study DBH (cm) H (m) Time 1 (months)

Time 2 (months)

Time 3 (months)

Time 4 (months)

Time 5 (months)

1 Populus 135 16 2 15 32 40 55

2 Populus 150 14 12 28 40

3 Celtis 120 8 11

equivalence between two normally distributed

populations.

3. Results

The graphs obtained are shown in Table 2. There

are clear differences between graphs obtained at 40

and 55 months (poplar 1) and 28 and 40 months

(poplar 2). In poplar 1, the graphs of April 2011 seems

to be different in comparison to the graphs of

December 2013; however, in this case, it may be due

to a weather effect (different seasons).

The nettle tree analysis was carried out in 3-D mode,

with two layers of sensors. The graphs show

differences in the lower layer of sensors, probably due

to their positions.

Table 3 shows statistical results of comparison

between tomograms. In the statistical analysis,

recorded sonic velocities between sensors were

compared. Each sensor transmits sound signals to all

other sensors. Therefore, for every couple of sensors,

we have two sound velocity values. The GLM test

shows values for test comparisons: between sonic

velocities obtained at two different times, for sensors

position and for statistical interaction of these

components. In red are noted the values with

statistical differences (statistical significance).

Table 2 Tomograms of trees.

POPLAR 1

JUNE 2012 AUGUST 2012

2 m

onth

s OCTOBER 2011 JANUARY 2013

15 m

onth

s

Tree Stability Evaluation: the Importance of Monitoring Programs

513

Table 2 to be continued

APRIL 2011 DECEMBER 2013

32 m

onth

s JANUARY 2013 MAY 2016

40 m

onth

s OCTOBER 2011 MAY 2016

55 m

onth

s POPLAR 2

JANUARY 2013 JANUARY 2014

12 m

onth

s

Tree Stability Evaluation: the Importance of Monitoring Programs

514

Table 2 to be continued

JANUARY 2014 MAY 2016

28 m

onth

s JANUARY 2013 MAY 2016

40 m

onth

s NETTLE TREE

APRIL 2013 MARCH 2014

11 m

onth

s

The position variable is significant in a few cases,

as interaction variable. These factors haven’t been

studied in this work, but the statistical analysis seems

to show that sometimes the module position is

statistically different between tests.

This analysis shows that there are statistical

differences between tomograms obtained at least after

2 years. There are no statistical differences between

analysis carried out at one year or less.

Tree Stability Evaluation: the Importance of Monitoring Programs

515

Table 3 Statystical comparison between tomograms.

TEST EFFECT F TEST P VALUE TEST EFFECT F TEST P VALUE

Poplar 1 Poplar 2

2 months test 1.5093 0.238169 12 months test 0.6289 0.438074

position 3.9418 0.005850 position 2.1736 0.054273

test x pos 0.8688 0.599867 test x pos 2.2698 0.001995

15 months test 1.986 0.180625 28 months test 42.5522 0.000004

position 2.626 0.040715 position 1.4878 0.203709

test x pos 0.855 0.608224 test x pos 2.9312 0.000046

32 months test 1.077 0.310196 40 months test 48.7770 0.000002

position 0.747 0.754682 position 2.2797 0.044491

test x pos 8.085 0.000000 test x pos 1.8269 0.019213

40 months test 51.076 0.000005 Nettle tree

position 0.827 0.636339 11 months test 2.072 0.166332

test x pos 1.577 0.082446 position 3.210 0.007280

55 months test 42.4990 0.000014 test x pos 0.331 0.997003

position 1.7455 0.154501

test x pos 1.3652 0.166640

4. Conclusion

In the tree stability evaluation, after a visual

analysis of trees, the arborist can choose different

instruments to analyze sections or parts of the tree to

define the extension of internal defect and its effect in

tree stability. The arborist also defines the monitoring

program, specifying which instrumental analysis and

timing are needed.

In this work, authors tried to define the minimum

interval between sonic tomographic analyses with

Arbotom.

In the three trees tested, all with internal decay,

authors are able to observe clear differences in

tomogram results after two years. This result is

supported by statistical analysis.

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