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From: Urša Vilhar, Egbert Beuker, Toshie Mizunuma, Mitja Skudnik, Fran�ois Lebourgeois, Kamel Soudani and Matthew Wilkinson, Tree Phenology.

In Marco Ferretti and Richard Fischer, editors: Developments in Environmental Science, Vol. 12, Amsterdam, The Netherlands, 2013, pp. 169-182.

ISBN: 978-0-08-098222-9 © Copyright 2013 Elsevier Ltd.

Elsevier

Author's personal copy

Chapter 9

Developments in Environmental Science, Vol. 12. http://dx.doi.org/10.1016/B978-0-

© 2013 Elsevier Ltd. All rights reserved.

Tree Phenology

Ursa Vilhar*,1, Egbert Beuker{, Toshie Mizunuma{, Mitja Skudnik*,Francois Lebourgeois}, Kamel Soudani} and Matthew Wilkinson||*Slovenian Forestry Institute, Ljubljana, Slovenia{Finnish Forest Research Institute Metla, Punkaharju, Finland{School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom}Laboratoire d’Etude des Ressources Foret Bois (LERFoB), AgroParisTech, ENGREF,

UMR1092, Nancy, France}Laboratoire Ecologie, Systematique et Evolution, CNRS, University of Paris Sud 11, Orsay,

France||Forest Research, Alice Holt Lodge, Farnham, Surrey, United Kingdom1Corresponding author: e-mail: [email protected]

Chapter Outline
9.1. Introduction 169
9.2. Objectives 170

9.3. Location of Measurements

and Observations 170

9.4. Variables to be Assessed 171

9.5. Measurements 171

9.5.1. Manual Phenological

Observations 171

9.5.2. Observations Using

Indirect Techniques 174

9.6. Quality Assurance 177

9.7. Examples of National

Applications 177

9.7.1. Trends in the

Growing Season

Length in Slovenia 177

9.7.2. Needle Appearance

in Finland 177

9.7.3. Modeling Climate

Change Effects on

French Plots 178

9.8. Conclusions 179

Acknowledgments 181

References 181

9.1 INTRODUCTION

Phenology has been defined as the study of cyclical biological events. In

plants, this can include flowering, leaf unfolding (or budburst), seed set and

dispersal, and leaf fall in relation to climatic conditions (Davi et al., 2011).

Plant phenology has been proposed as an indicator of climatic difference

08-098222-9.00009-1

169

http://dx.doi.org/10.1016/B978-0-08-098222-9.00009-1

SECTION III Monitoring Methods for Above-Ground Vegetation170


and global change by the European Environmental Agency and the Intergovern-

mental Panel on Climate Change (IPCC, 2007). Long-term phenological

records in trees, including spring events such as leaf unfolding and autumnal

events of leaf coloring, have shown that a rise in global temperature generally

leads to earlier timing of spring events (Chen and Xu, 2012; Doi and Katano,

2008; Menzel et al., 2006). Leaf phenology being a major determinant of water

and CO2 fluxes, recent studies have clearly shown that growing season length

controls net ecosystem primary productivity and that phenological shifts have

already modified the annual carbon (C) cycle of terrestrial ecosystems (Davi

et al., 2006). This chapter describesmethodologies for harmonized phenological

assessments as adopted by the International Co-operative Programme on

Monitoring and Assessment of Air Pollution Effects on Forests (ICP Forests)

and provides examples of results obtained in four European countries.

9.2 OBJECTIVES

One of the important purposes of phenological observations is to improve

understanding of the relationships between meteorological variables and asso-

ciated biological responses. Understanding these relationships is crucial, not

only for predicting ecosystem responses to climate change but also in identi-

fying the C-uptake period (of forest stands) and for examining the seasonal

exchanges of water and energy between the land surface and the atmosphere

(Chen and Xu, 2012). The main objectives of phenological observations at

forest intensive monitoring plots are:

1. to provide information on the status and development of forest trees over

the course of the year;

2. to determine the course of the annual development stages of forest trees

and their dependence on local (e.g., meteorological and site) conditions

including damaging events;

3. to document and explain possible changes in the timing of these stages in

relation to environmental factors of natural and/or anthropogenic origin

such as air pollution and climate change.

9.3 LOCATION OF MEASUREMENTS AND OBSERVATIONS

Within the ICP Forests, phenological observations are usually made within the

plot and/or in a buffer zone (see Chapter 6). All species within a monitoring plot

are of interest, though priority is usually given to the dominant species.

Phenological observations can be carried out on plot level or may focus on

single trees within the plot, depending on the objectives. Phenological obser-

vations at the plot level are performed as a cursory examination of the forest

crowns of several trees on the plot, preferably on more locations per plot.

Phenological observations at the individual tree level are carried out on a

selected number of trees. Within the ICP Forests, at least 10 trees of the major

species per plot are selected. These trees should be dominant or codominant.

Chapter 9 Tree Phenology 171


Good visibility of the upper part of the crown is a prerequisite. The use of

binoculars is recommended. In case the upper crowns are not visible, assess-

ments on the middle part of the crown are also acceptable. The same part of

the crown is considered for subsequent phenological observations throughout

the year, as well as in following years. Preference is given to those trees on

which crown and growth condition assessments are also carried out (see

Chapters 8 and 10). When an insufficient number of crown condition trees

are available, additional trees from within the plot or buffer zone are selected.

Trees used for leaf or needle sampling are not included (see Chapter 12).

Trees used for phenological assessments are either permanently marked in

the field or must be clearly identified, for example, by their coordinates.

9.4 VARIABLES TO BE ASSESSED

All phenological phases and records are of interest for phenological monitor-

ing. However, from a practical point of view (e.g., financial input, ease and

reliability of the monitoring, Europe-wide comparability, compatibility with

other surveys such as crown condition assessments) it is necessary to concen-

trate on a limited set of phases: occurrence of flushing, flowering, Lammas

shoots (or secondary flushing), color change, and leaf/needle fall. Distinctions

are made between conifers and broad-leaved species (Table 9.1). Also, biotic

(pests and/or diseases) and abiotic damage (e.g., frost, wind, hail) relevant to

phenological development of trees are considered.

The application of a classification system can help making the assessments

comparable between different teams operating in different countries. Five

classes have proven to be operational within the European forest monitoring

system. Within the observed tree crowns on plot level or on single trees, the

proportion of needles or leaves at each phenological phase is recorded using

the following classes:

1. Not observed (<1% of the observed part of the crown);

2. Infrequent or slight (1–33% of the observed part of the crown);

3. Common or moderate (>33–66% of the observed part of the crown);

4. Abundant or severe (>66–99% of the observed part of the crown);

5. Whole (100% of the observed part of the crown).

A photoguide with phenological stages for various tree species is available

(http://www.metla.fi/eu/icp/phenology). Figure 9.1 gives an example for nee-

dle appearance in conifers and leaf unfolding in broadleaves.

9.5 MEASUREMENTS

9.5.1 Manual Phenological Observations

The recording concentrates on obvious events based on a brief examination in the

forest stands. Assessments are carried out during the individual phases of pheno-

logical phenomena. The assessment is repeated until the phase is complete.

http://www.metla.fi/eu/icp/phenology

TABLE 9.1 Phenological Phases

Conifers Broad-leaved species

Needle appearance Leaf unfolding

Starts when the fresh green needlesbecome visible from the buds.

Starts when the fresh green leavesbecome visible from the buds.

Lammas shoots Secondary flushing

Starts when flushing of needles occurslater and clearly distinct from springflushing.

Starts when flushing of leaves occurslater and clearly distinct from springflushing.

Flowering Flowering

Starts with beginning of opening ofthe male flowers (characterized bypollen shed).

Starts with beginning of opening of themale flowers.

Autumn coloring (just for some species,e.g., Larix decidua Mill.)

Autumn coloring (except someevergreen species, e.g., Quercus ilex L.)

Starts when the color of the needles start tochange from green into yellow, red,orange, or brown during late summer andautumn.

Starts when the color of the leaves start tochange from green into yellow, red,orange, or brown during late summerand autumn.

Leaf fall (just for some species, e.g., Larixdecidua Mill.)

Leaf fall

Starts when the needles drop from the tree. Starts when the leaves drop from the tree.



FIGURE 9.1 Needle appearance: (A) Pinus sylvestris (L.) and (B) Picea abies (L.) Karst. Leaf

unfolding: (C) Fagus sylvatica (L.) and (D) Quercus sp. (Photos by Ursa Vilhar).





Phenological observations at the plot level can be carried out by technical

staff. They are not cost intensive as they may be conducted in parallel with the

collection of samples taken at the plots in regular time intervals. A frequency

of at least once every two weeks during the growing period is recommended.

Phenological observations at the individual tree level are carried out at least

once a week during the critical phases, but daily observations are the optimum.

9.5.2 Observations Using Indirect Techniques

9.5.2.1 Terrestrial Digital Image Photography

As the relative costs of digital cameras have decreased and image quality has

increased, the use of digital cameras for forest phenology monitoring has been

adopted rapidly. The advantages of remote digital camera systems over tradi-

tional manual observations are numerous: first, there is a reduced need for an

observer to make regular site visits, which can often be costly and time con-

suming, especially in remote forest areas. Second, the high frequency with

which camera-based systems can capture canopy images, for example, daily

or even hourly makes the accurate observation of key phenological events

more likely. Third, by using the digital values of red, green, and blue taken

from the image file, the time course of vegetation indices can be quantita-

tively analyzed (Richardson et al., 2007).

Several different types of digital camera systems have been used across a

range of sites to record high frequency phenological images. A test using 11 dif-

ferent cameras at a deciduous forest in North America showed that the choice of

camera and image format did not make large differences in phenological record-

ing (Sonnentag et al., 2012). Digital camera systems can be used to provide a

permanent photographic record suitable for manual inspection. These photos

can be compared with images or descriptions of standard phenological stages

(Table 9.1), an approach which potentially removes the biases associated with

field-based assessments reliant on multiple observers. Using color strength,

optical vegetation indices can be determined (e.g., Richardson et al., 2007).

The time courses of these vegetation indices are useful in detecting key pheno-

logical events, and where photos from several years are available, these can be

used to quantify the interannual variation (Figures 9.2 and 9.3). In this example,

differences in the timing of the green-up pattern over 3 years at an oak forest in

the south east of England are evident, with the earliest spring occurring in 2011;

damage to the canopy caused by a late air frost in 2010 can also be seen. Despite

variations that may occur due to changes in light conditions, camera-based tech-

niques can provide useful information about the timing of key phenological

events such as budbursts or leaf fall.

9.5.2.2 Applying the Normalized Difference Vegetation Index

For many years, spectral vegetation indices (SVI) that combine visible and

near infrared light reflected by vegetation, such as the normalized difference

0.5

0.4

0.3

Rel

ativ

e g

reen

(ra

tio)

0 60 120

Day of year

180 240 300 360

2009 2010 2011

FIGURE 9.2 Relative proportion of green in digital images of an oak forest canopy taken at the

Alice Holt Research Forest in the South of England. The dotted line shows the difference in can-

opy state on day 124 (4th of May) in each year.

FIGURE 9.3 Photographs of an oak forest canopy at the Alice Holt Research Forest in the South

of England, the images were taken on day 124 (4th of May) in 2009, 2010, and 2011. The main

photos were taken using a NetCam SC 5 MP, inset photos were taken using an automatic-

capturing digital fisheye camera (ADFC) system, part of the Phenological Eyes Network (http://

pen.agbi.tsukuba.ac.jp/index_e.html).



vegetation index (NDVI), have been used to quantify the phenology of differ-

ent ecosystems from ground- (Soudani et al., 2012) and satellite-based mea-

surements (Soudani et al., 2008). NDVI is determined from red and near

infrared radiances measured above the canopy and is commonly used in

remote sensing studies because it is sufficiently sensitive to capture small

changes in the amount of vegetation greenness. In the framework of the

French network of long-term measurements of carbon, water, and nutrients

fluxes, many forest sites have been equipped with laboratory-made NDVI sen-

sors to monitor the temporal dynamics of canopy structure and phenology

with daily resolution (Soudani et al., 2012). This approach has been applied

to other herbaceous and forest ecosystems across the world. Results show that

NDVI time series provide effective estimates of photosynthetic biomass and

constitute accurate estimates of dates of main phenological events such as

http://pen.agbi.tsukuba.ac.jp/index_e.html

http://pen.agbi.tsukuba.ac.jp/index_e.html



budburst and leaf yellowing in both deciduous and evergreen forests

(Figure 9.4A and B).

In the field of satellite-based remote sensing of vegetation phenology, phe-

nological metrics are also derived from time series of NDVI or other SVI

data. Satellite remote sensing has the main advantage of being the only way

1.0A

2005 Fr - FontainebleauOak Forest2006

200720082009

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.230 60 90 120 150 180

Day of year210 240 270 300 330 360

ND

VI

300.4

0.5

0.6

0.7

0.8

0.9

1.0

60 90 120

Day of year

B

ND

VI

150

200720082009

Fr- Puéchabon Forest

180 210 240 270 300 330 360

FIGURE 9.4 (A) Ground-based NDVI time series for a deciduous forest of sessile oak (South of

Paris, France) and (B) Ground-based NDVI time series for an evergreen forest of holm oak (South

of France). Source: Soudani et al. (2012).



allowing assessment of phenology over large and inaccessible regions. The

main obstacle limiting the routine use of this technique to assess forest phe-

nology is the presence of clouds that can make satellite data completely unus-

able or introduce noise into time series reducing the prediction accuracy of

phenological events (Soudani et al., 2008).

9.6 QUALITY ASSURANCE

Adequate Quality Assurance (QA) is of great importance, especially for the

long-term phenological observations at large number of observation plots.

For the manual phenological observations, training and intercalibration of

the field staff at national and international level is required (Beuker et al.,

2010). Procedures for QA in phenology (field manual and forms, training,

quality objectives, field and photo intercalibration exercises, independent con-

trol survey) are reported in Chapter 21, together with results from an interna-

tional test.

9.7 EXAMPLES OF NATIONAL APPLICATIONS

9.7.1 Trends in the Growing Season Length in Slovenia

In Slovenia, phenology assessments on intensive forest monitoring plots

started in 2004. Time series are still too short to report on long-term changes

in the length of the growing season. In a first analysis, however, the beginning

and the end of the growing season were investigated for intensive forest mon-

itoring plots with European beech (Fagus sylvatica L.) as dominant tree spe-

cies (Vilhar et al., 2012). On five intensive forest monitoring plots,

phenological observations at the individual tree level were carried out on

20 trees from 2004 till 2011. The length of the growing season was deter-

mined as number of days between the beginning of leaf unfolding and the

beginning of autumn coloring. Overall beginning of the growing season was

shown to start earlier (d¼�0.36; p<0.01) and end of the growing season

was later (d¼0.24; p<0.01) in more recent years at all sites. This resulted

in a significant prolongation of the growing season for European beech on

selected sites (d¼0.35; p<0.01) (Figure 9.5).

9.7.2 Needle Appearance in Finland

For evergreen conifer tree species, such as Norway spruce (Picea abiesL. Karst) and Scots pine (Pinus sylvestris L.) the determination of both the

beginning and the end of the growing season is not possible using phenology

observations. In spring needle, appearance gives an indication for the begin-

ning of growth, but in autumn, no obvious phenological phase occurs. In

Finland, phenology assessments were made at a number of Norway spruce

FIGURE 9.5 Trend analysis for the length of the growing season for European beech (Fagus

sylvatica L.) on five intensive forest monitoring plots in Slovenia. Box–Whisker plots for mean

values and standard deviation for 20 trees per plot are shown. Source: Vilhar et al. (2012).



and Scots pine intensive monitoring plots. Although the longest series of

observations for a Norway spruce plot in Punkaharju are more than 10 years,

for neither of the two species a significant change in the timing of needle

appearance could be found (Figure 9.6).

9.7.3 Modeling Climate Change Effects on French Plots

After modeling the large-scale climate response patterns of leaf unfolding,

leaf coloring, and growing season length of evergreen and deciduous French

temperate trees using a nonparametric procedure, the effects of eight future

climate scenarios on phenological events were predicted (Lebourgeois et al.,

2010). The modeling procedure known as “Random Forests” is a combination

of tree predictors such that each tree depends on the values of a random vector

sampled independently and with the same distribution for all trees in the forest

FIGURE 9.6 Beginning of growing season expressed as mean starting dates of needle appear-

ance in Norway spruce and Scots pine at two intensive monitoring plots at Punkaharju, Finland.

Box–Whisker plots for mean values and standard deviation for 10 trees per plot are shown.



(Breiman, 2001). The ground observations from 103 temperate forest intensive

monitoring plots (10 species and 3708 trees) from the French intensive forest

monitoring network “RENECOFOR” were used for the period 1997–2006.

“Random Forests” models were applied to predict phenological events from

climatic and ecological variables. Based on the model results, maps of pheno-

logical events throughout France were drawn assuming present climate and also

two different climate change scenarios of the Intergovernmental Panel on Cli-

mate Change (A2, B2) and four global circulation models (HadCM3, CGCM2,

CSIRO2, and PCM). Current observations and predicted values for the periods

2041–2070 and 2071–2100 were compared (Figure 9.7). At the end of the

twenty-first century, each model predicts earlier budburst (mean: 7 days) and

later leaf coloring (mean: 13 days) leading to an average increase in the growing

season of about 20 days (for oaks and beech stands). The A2-HadCM3 hypoth-

esis leads to an increase of up to 30 days in many areas. As a consequence of

higher predicted warming during autumn than during winter or spring, shifts

in leaf coloring dates appear greater than trends in leaf unfolding.

9.8 CONCLUSIONS

Phenological observations are crucial for assessing cyclical biological events.

Phenological observations on trees of forest monitoring plots provide informa-

tion on the status and development of trees during the year that can be related

to other (environmental) assessments made at the same plots or even the same

trees. In order to assess changes in the timing of phenological events long-

term observations are needed. Digital cameras can be used to provide a

FIGURE 9.7 Predicted variations (in days) at the end of the twenty-first century in spring leaf

unfolding (LU), autumn leaf coloring (LC), and length of the growing season (LGS). Each map

corresponds to the difference between the average values from the period 1991 to 2000 and the

average predicted values with the climatic scenario A2-HadCM3 for the period 2071–2100. For

LU, negative values correspond to more precocious dates of bud burst. For LC and LGS, positive

values correspond, respectively, to a delay of autumn leaf coloring and a lengthening of the grow-

ing season. From: Lebourgeois et al. (2010).





permanent photographic record at plot level suitable for manual inspection—an

approachwhich potentially removes the biases associatedwith field-based assess-

ments reliant on multiple observers. By using the digital information contained

within photos, it has now become possible—with some precautions—to quantify

the phenology of trees and forests with a much higher temporal (e.g., daily) and

spatial resolution, even enabling monitoring at the individual tree level. Vegeta-

tion indices, such as the NDVI have been used for many years to quantify the phe-

nology of different ecosystems. Ground-based NDVI time series describe the

temporal changes of canopy structure at very high frequency and constitute a

direct and an effectiveway to extract the dates ofmain phenological events in both

deciduous and evergreen forests. On the contrary, the use of satellite data encoun-

ters serious difficulties especially due to the presence of clouds. Spatial resolution

at which these data are available is often not sufficiently high for site-specific

forest monitoring.

All indirect methods using optical vegetation indices from digital camera

or NDVI sensors need to be validated against ground observations, for which

tree phenological observations from the forest monitoring plots are often used.

A number of national results are presented pointing to effects of climate

change on phonological phases in Slovenia and France, but on conifer ICP

Forests Level II plots in Finland no significant changes were found so far.

Work is ongoing to apply similar evaluations to the Europe-wide data sets.

ACKNOWLEDGMENTS

Data collection for derivation of national results presented was partly cofinanced under the

Lifeþ project LIFE07 ENV/D/000218 FUTMON “Further development and implementa-

tion of an EU-level forest monitoring system.”

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