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The comparison of several colour indices for the photographic recording of canopy phenology of Fagus crenata Blume in eastern Japan


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Background: To understand how forests and woodland respond to global climate change, phenological observations are being made at a number of sites worldwide. Recently, digital cameras have been deployed as part of the existing network of ecosystem CO2 flux towers to provide a time-series of canopy images, and various numerical indices have so far been used by different authors.Aims: To identify which are the most effective colour indices to calculate from the signals extracted from digital cameras, in order to provide recommendations to the scientific community.Methods: Sample images of a Japanese beech (Fagus crenata) forest on Mt. Tsukuba (Japan) were used to define and calculate 12 colour signals and vegetation indices.Results: Although the strength of green signal and green excess index were reliable indicators for estimating foliage growth period, the indices were susceptible to low-visibility weather conditions and distance from the camera. Hue provided a robust metric, showing much less scatter during the vegetative period and a good indication of spring bud break. The bud break dates derived from the indices were slightly earlier than those assessed by visual observation, while the abscission dates were later.Conclusions: We propose that of all the candidate colour indices, hue is the most promising for the detection of bud break as it was least affected by atmospheric conditions.
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The comparison of several colour indices for the photographic recording of
canopy phenology of <i>Fagus crenata</i> Blume in eastern Japan
Toshie Mizunumaa; Tomokazu Koyanagib; Maurizio Mencuccinia; Kenlo N. Nasaharab; Lisa Wingatec;
John Gracea
a School of GeoSciences, University of Edinburgh, Edinburgh, UK b Institute of Agricultural and Forest
Engineering, University of Tsukuba, Tsukuba, Japan c School of the Biological Sciences, University of
Cambridge, Cambridge, UK
Accepted uncorrected manuscript posted online: 04 March 2011
First published on: 07 April 2011
To cite this Article Mizunuma, Toshie , Koyanagi, Tomokazu , Mencuccini, Maurizio , Nasahara, Kenlo N. , Wingate, Lisa
and Grace, John(2011) 'The comparison of several colour indices for the photographic recording of canopy phenology of
<i>Fagus crenata</i> Blume in eastern Japan', Plant Ecology & Diversity, 4: 1, 67 — 77, First published on: 07 April 2011
To link to this Article: DOI: 10.1080/17550874.2011.563759
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Plant Ecology & Diversity
Vol. 4, No. 1, March 2011, 67–77
The comparison of several colour indices for the photographic recording of canopy phenology of
Fa g u s c r e n a t a Blume in eastern Japan
Toshie Mizunumaa*, Tomokazu Koyanagib, Maurizio Mencuccinia, Kenlo N. Nasaharab, Lisa Wingatecand John Gracea
aSchool of GeoSciences, University of Edinburgh, Edinburgh, UK; bInstitute of Agricultural and Forest Engineering, University of
Tsukuba, Tsukuba, Japan; cSchool of the Biological Sciences, University of Cambridge, Cambridge, UK
(Received 24 June 2010; final version received 14 February 2011)
Background: To understand how forests and woodland respond to global climate change, phenological observations are
being made at a number of sites worldwide. Recently, digital cameras have been deployed as part of the existing network of
ecosystem CO2flux towers to provide a time-series of canopy images, and various numerical indices have so far been used
by different authors.
Aims: To identify which are the most effective colour indices to calculate from the signals extracted from digital cameras, in
order to provide recommendations to the scientific community.
Methods: Sample images of a Japanese beech (Fagus crenata) forest on Mt. Tsukuba (Japan) were used to define and calculate
12 colour signals and vegetation indices.
Results: Although the strength of green signal and green excess index were reliable indicators for estimating foliage growth
period, the indices were susceptible to low-visibility weather conditions and distance from the camera. Hue provided a robust
metric, showing much less scatter during the vegetative period and a good indication of spring bud break. The bud break
dates derived from the indices were slightly earlier than those assessed by visual observation, while the abscission dates were
Conclusions: We propose that of all the candidate colour indices, hue is the most promising for the detection of bud break as
it was least affected by atmospheric conditions.
Keywords: canopy phenology; deciduous broadleaved forest; digital image; HSL; RGB
To understand how vegetation responds to climate warm-
ing, remote surveillance techniques have been established
to record continuously the long-term patterns of phenol-
ogy, including bud break, flowering and leaf abscission,
and relate these phenological events to the physiological
activity of the canopy (Richardson et al. 2007; Wingate
et al. 2008; Morisette et al. 2009). Ultimately, we expect
this will be achieved by using satellites; however comple-
mentary ‘ground truth’ approaches such as citizen-based
observations (e.g. the GLOBE programme in the UK, or digital camera networks
(Wingate et al. 2008; Jacobs et al. 2009; Graham et al.
2010) are also essential to verify phenological patterns.
Recently, there have been attempts to analyse the colour
signals, red, green and blue (RGB), obtained from commer-
cially available digital camera images across many different
ecosystems (Richardson et al. 2007, 2009; Ahrends et al.
2008, 2009; Maeda et al. 2008; Jacobs et al. 2009; Ide
and Oguma 2010; Graham et al. 2010). So far, there is no
consensus on how these signals should be combined to
resolve adequately the key phenological events, such as
bud break and leaf abscission. There are many possible
indices based on the RGB signals. Maeda and Gamo (2004)
have patented a system that uses separate RGB indices in
a video system. Green fraction is sometimes used to detect
*Corresponding author. Email:
foliage phenology (e.g. Ahrends et al. 2008, 2009), while
red fraction has an advantage to detect autumn colouration
(Richardson et al. 2009). Others meanwhile have devised
vegetation indices analogous to the familiar Normalised
Difference Vegetation Index (NDVI) retrieved from satel-
lite spectral data. Normalised difference between red and
blue, which has a good correlation with the chlorophyll
content of crop plants (Kawashima and Nakatani 1998),
has been used as a weather filter of outdoor images (Ide
and Oguma 2010). The seasonal pattern in green excess
index (GEI) was correlated with data of gross primary pro-
ductivity of a forest (Richardson et al. 2009). Of particular
interest is the attempt to use the HSL (Hue, Saturation,
Lightness) colour system as the basis of an alternative
index. The HSL concept of describing colour by its inten-
sity and brightness in three-dimensional space was devised
by Smith (1978) as an early contribution to computer
graphics, and is said to be similar to the colour-recognition
system of the human eye. Previously, the HSL system has
been applied to phenological research to process spectral
data (Kodani et al. 2002), but more recently Graham et al.
(2009) used it to detect bud break and measure leaf area
expansion in Rhododendron occidentale Torr. & A. Gray.
Using viewpoints within the canopy, Graham et al. (2009)
found the hue-based colour system to be more useful than
the RGB colour system for their site.
ISSN 1755-0874 print/ISSN 1755-1668 online
© 2011 Botanical Society of Scotland and Taylor & Francis
DOI: 10.1080/17550874.2011.563759
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68 T. Mizunuma et al.
However, more tests are now required to evaluate the
applicability of these diverse approaches, and to assess
which of them is best related to phenological processes
and ideally to the seasonal cycle of forest CO2fluxes. We
present a detailed study of a particular case, using a num-
ber of indices obtained from a time-series of images. We
ask the general question: what is the best way to repre-
sent colour signals as an index? We examine recent data
of Fagus crenata Blume from a case study in a temperate
forest in Japan, collected by the Phenological Eyes Network
(PEN,; Tsuchida
et al. 2005; Nishida 2007).
Materials and methods
Mt. Tsukuba is located 20 km north-east of Tsukuba city,
Japan. A meteorological observation station was installed
in January 2006 on the top of the west peak of Mt. Tsukuba
(361330 N, 140552.8 E, 868 m above sea level)
by the University of Tsukuba (Hayashi et al. 2006). From
2007 onwards, a digital camera was mounted on a rooftop
terrace by the PEN to observe the seasonal change of adja-
cent trees. The mountain is covered by mixed forests of
Japanese beech (Fagus crenata), Japanese mountain maple
(Acer palmatum Thunberg) and a variety of conifers such
as Japanese cedar (Cryptomeria japonica D. Don) and
Japanese red pine (Pinus densiflora Siebold et Zucc). In
2008, the mean annual temperature was 9.9 C, the mean
temperature in August was 20.5 C and the annual precip-
itation was 1382 mm; the annual mean temperature and
the annual amount of precipitation were within the nor-
mal range, with low rainfall in July and heavy rain in
Digital camera images
A commercially available compact digital camera (Coolpix
4500, 4.0 million pixels, Nikon Corp., Japan) was installed
within a waterproof housing at 12 m height, pointing
toward north-west, using the camera’s original lens (focal
length 7.85–32 mm) according to the protocol of PEN
(Tsuchida et al; 2005, Nishida 2007), and focusing on a
target F. crenata tree in a dense forest on the western
slope of Mt. Tsukuba. Canopy images of the same scene
were taken at 12:00 local time and stored as a compressed
JPEG file (resolution 2272 ×1704 pixels, three chan-
nels of 8-bit RGB colour information, and ‘fine’ image
quality by the setting of camera). Images were captured
in automatic aperture and exposure mode and white bal-
ance was fixed to ‘cloudy’. This study was based on the
images captured in 2008. Images for the periods from
Day-of-Year (DOY) 63 to 96, from DOY 210 to 219 and
from DOY 362 to 366 were missing due to system fail-
ure. A total of 315 days of images were incorporated in our
Image analysis
A customised MATLAB program (7.7.0, R2008b; The
MathWorks) developed by AD Richardson and JP Jenkins
(cf. Richardson et al. 2007) was modified to process the
image files. Sample images and the region of interest (ROI)
used for this analysis are shown in Figure 1. The ROI was
set to three separate regions: (1) a prominent large beech
tree, well in focus; (2) a beech canopy to the left and behind
the main tree; and (3) a distant beech tree near the boundary
of the forest and the sky (Figure 1). Images were classi-
fied into three groups determined by weather conditions;
(1) images under high solar radiation conditions; (2) images
under low solar radiation conditions; and (3) low-visibility
images corresponding to days with haze and rain drops on
the window of the water-proof housing. Kawashima and
Nakatani (1998) reported different distribution patterns in
frequency of pixel values between images taken on a clear
day and those taken on a cloudy day. Although other studies
often excluded low-visibility images, we analysed them to
examine the sensitivity of colour indices to wet conditions.
Images taken during high and low solar radiation condi-
tions were identified using the data of downward shortwave
radiation measured every 10 min (EKO/Photo diode). A
threshold of 833.3 W m2(0.5 MJ m2per 10 min) was
used to separate high-radiation days from low-radiation
days. Low-visibility images were identified ‘by eye’.
Information from the 8-bit colour digital images
consisted of three values, each a digital number between
0 and 255, representing red, green and blue. We extracted
RGB values for each pixel of each image, from which the
mean of all pixel values for a given ROI was calculated.
A number of indices (equations are listed in Table 1)
were calculated to explore the suitability of each index for
observing changes in canopy phenology. This list of indices
is not exhaustive, but it includes those indices which have
been debated at recent phenology meetings. The relative
Figure 1. Example image (DOY =176). The white squares on
the image indicate the regions of interest. The camera was point-
ing north-west, and the overall field of view contains about 1 ha
of forest. The foreground is about 20 m away.
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Comparison of colour indices of Fagus images 69
Table 1. Definitions of the signals and the derived vegetation indices from the digital camera images. See also Figure S1, available
Signals and indices Formulation References
Strength of signal in RGB channel Schannel =nchannel/(nred +ngreen +nblu e) Richardson et al. (2007)
HSL signals
(Double hexagonal pyramid model)
Hue (degree)
Takagi and Shimoda (1991),
Joblove and Greenberg (1978)
H=(br)/((Imax Imin)×60) +120 if g=Imax
H=(rg)/((Imax Imin)×60) +240 if b=Imax
H=(gb)/((Imax Imin)×60) +360 if g<b
H=(gb)/((Imax Imin)×60) otherwise
Saturation S=(Imax Imin)/(Imax +Imin ) if lightness 0.5
S=(Imax Imin)/(2 (Imax +Imin )) if lightness >0.5
S=0ifImax =Imin
Lightness L =(Imax +Imin)/2
Monochrome luminance M=0.2989r+0.5870g+0.1140bSMPTE (2004)
Vegetation indices
Green Excess Index GEI =2n
green(nred +nblu e) Woebbeche et al. (1995),
Richardson et al. (2007)
Red Excess Index REI =1.4 nredng reen Meyer and Camargo Neto (2008)
Green Red Vegetation Index GRVI =(ngreen nred)/(ngreen +nred ) Falkowski et al. (2005),
Kawashima and Nakatani (1998)
Green Blue Vegetation Index GBVI =(ngreen nblue )/(ngreen +nblue ) Kawashima and Nakatani (1998)
Red Blue Vegetation Index RBVI =(nrednbl ue)/(nred +nbl ue) Kawashima and Nakatani (1998)
Note: nred, ngreen, nblue are the values of red, green and blue respectively and nchannel is one of these; r=nred /255, g=ngreen /255, b=ngreen /255; Imax
is maximum value of r,gand band Imin is minimum value of r,gand b.
Cited in Meyer and Camargo Neto (2008).
strength of the signals in each channel was expressed as the
ratio of the values of each colour relative to the total values
of all three colours. In order to compare different colour
systems, the values of the RGB channels were converted
into hue, saturation, and lightness for the HSL colour
system (Joblove and Greenberg 1978) and monochrome
luminance following the National Television System
Committee (NTSC) standard (SMPTE, 2004). RGB and
HSL colour spaces used are explained diagrammatically
in Figure S1 of the supplementary materials (available on
the supplementary content tab of the article’s online page
Classical observations of phenology made by human
observers are normally used to define particular phenolog-
ical phases (e.g. data from the International Phenological
Gardens in Europe). We compared the various dates of
phenological events obtained from visual inspection and
from the indices obtained from the digital camera images.
To compare the seasonal patterns of indices between the
different regions of interest, we excluded the data of low-
visibility images and calculated the 5-day running averages.
The dates of bud break and abscission were estimated by
detecting an abrupt increase or decrease in the indices.
Individual images were observed by eye; the day on which
leaves first appeared in each ROI was defined as the day
of bud break, and the day all leaves disappeared in each
ROI was defined as the day of abscission (sample images
are shown in Figure S2, available online). The dates for
the indices were manually determined by reading graphs;
the day that the trend started to change from winter state
was used for the bud break estimation, and the day that the
trend returned to the winter state was used for the abscis-
sion. Most indices showed a peak or trough in spring and
autumn, with some deviation from the dates of bud break
and abscission. We estimated peaks of spring and autumn
for each index using maximum or minimum values of the
running average data.
Seasonal pattern of RGB values and indices
We first report the basic data from the regions of inter-
est delineated in Figure 1. The red, green, and blue values
extracted from image files for a region of the dominant
beech tree are shown in Figure 2. Both red and blue values
decreased sharply when leaf-flushing started. Thereafter
the red values stabilised over the summer period until a
dramatic increase associated with autumn leaf colouration
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70 T. Mizunuma et al.
Figure 2. Red, green and blue (RGB) values extracted from
images in 2008 for one of the regions of interest, Tree1a, for a cen-
tred beech tree. In the graphs of values, solid circles indicate data
from images captured when the downward shortwave radiation
was low, open circles indicate images under high-radiation condi-
tions, and crosses indicate low-visibility images obscured by haze,
rain and/or water droplets (low-visibility images were selected
manually after visual inspection). The dotted line on DOY 122
indicates the visually selected date for budburst determined using
the images on the left, and DOY 318 indicates the selected date
for abscission using the images on the right.
was observed. The blue values increased slightly during
midsummer but declined again in autumn. In contrast, the
green values remained comparatively constant throughout
the year, with the exception of a slight decrease detected
in autumn. The values for low-visibility images showed
greater dispersion; in particular, the blue values were sus-
ceptible to atmospheric visibility in the foliage period.
The seasonal changes in 12 colour indices from the
dominant beech tree derived from the values are shown
in Figure 3. The meaning of the indices is defined math-
ematically in Table 1. The strength of the red channel
was stable over most of the year except during autumn,
when a peak was observed for autumn colouring and there-
after decreased with abscission (Figure 3A). The strength
of the green signal declined gradually over the summer
and reached the level of dormancy values after a small
negative inflection (Figure 3B). The strength of the blue
signal showed a negative bimodal distribution with inverted
peaks in spring and autumn (Figure 3C). The dispersion by
low visibility clearly is seen in the strength of green as well
as the strength of blue.
The three signals obtained for the HSL colour sys-
tem showed quite different seasonal patterns. Hue was the
least variable from day-to-day of the three (Figure 3D). It
dramatically increased after day 120, which was consis-
tent with the change observed in the green signal strength
shown in Figure 3B. Thereafter, while the strength of the
green signal gradually decreased over the season, the hue
signal continued to increase during the leaf-flushing stage
until day 130; thereafter it declined gradually during the
remainder of the season (Figure 3D). Hue was affected
by precipitation, but overall it was less ‘noisy’ than other
indices. In winter, the hue was affected by snow cover even
on clear days. The saturation signal was more suscepti-
ble to visibility than that of the hue (Figure 3E), but its
seasonal pattern depicted two obvious maxima that indi-
cated the peaks of leaf-flushing and autumn colouration.
Saturation values tended to be lower during hazy con-
ditions. Lightness values also displayed large day-to-day
variations, with maximal values occurring during the dor-
mant period (Figure 3F). Monochrome luminance (Figure
3G) showed a similar trend to that of the lightness. The dif-
ference between obscure and clear images was hard to dis-
tinguish for luminance. Although luminance was expected
to show the high density of leaves in the green period, it
showed relatively little seasonal variation and was quite
The Green Excess Index (GEI) effectively detected bud
break, showing a dramatic increase at the spring onset;
thereafter a continuous decline was observed throughout
the season, with a slightly steeper drop in GEI observed
during the autumn senescence period (Figure 3H). The
Red Excess Index (REI) decreased in the leaf-flushing
phase and showed a peak in autumn (Figure 3I). Over the
season the Green Red Vegetation Index (GRVI) flipped
from negative to positive at leaf-flushing, and showed a
sharp decrease back to negative values during leaf shed-
ding (Figure 3J). Interestingly, the most negative values
occurred prior to leaf abscission and were more negative
than dormancy values. Both the Green Blue Vegetation
Index (GBVI) and the Red Blue Vegetation Index (RBVI)
had a bimodal distribution in spring and autumn showing
trends that were similar to those observed for the saturation
signal (Figure 3K, L). The dispersion by low visibility is
seen in GEI, GBVI and RBVI.
Low-visibility conditions added noise to most indices
except strength of red, hue and GRVI, while the differ-
ence between the high and low-light conditions was not
significant. To quantify fluctuation, we calculated the first
derivative for the moving average index excluding low-
visibility images (Figure S4, available online). Hue showed
the least day-to-day variability, followed by strength of
green. Lightness and monochrome luminance showed a
day-to-day variability throughout the year, which suggested
the difficulty to detect signals of seasonal change.
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Comparison of colour indices of Fagus images 71
Figure 3. Seasonal variations in the colour indices calculated (listed in Table 1) in 2008 for Tree1a. Legend as in Figure 2.
Spatial variation in seasonal pattern of indices and
phenological dates
We combined data of all ROIs into one graph, to illustrate
how much the indices varied when the tree examined is in a
non-optimal position in the image, being partially shaded or
so far away as to be affected by haze (see Figure 1). To make
the data easier to observe on the graph, the 5-day running
averages were plotted rather than the daily data shown in
the previous figure (Figure 3). The different ROIs showed
similar trends to each other (Figure 4). Hue showed the least
scatter. A study in North American deciduous forests found
that the seasonal patterns in strength of red, green and blue
at more distant stands were less pronounced (Richardson
et al. 2009). The same trend appeared in the patterns of all
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72 T. Mizunuma et al.
Figure 4. Seasonal variations in the colour indices calculated (listed in Table 1) in 2008 for all regions of interest extracted from images
under high-radiation conditions. Hue values over 180were converted into negative from 0(e.g. 350->10).
indices; the signals from Tree1 were the strongest followed
by Tree2 and Tree3 corresponding to the distance from the
camera. Green fraction was significantly influenced by the
distance, while hue was less sensitive to the distance.
The estimated phenological dates derived from the sea-
sonal patterns of the indices are presented in Figure 5,
with the dates of bud break and abscission obtained from
visual inspection of the images. Most of the bud break
signals derived from images occurred before the visual
identification, while most of the abscission signals were
later (Figure 5A, B). Consequently, the foliage periods visu-
ally determined tended to be shorter than those from images
(Figure 5C). Most indices showed an agreement on the
spring peak date, while the peaks of the hue, the REI and
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Comparison of colour indices of Fagus images 73
Figure 5. Phenological dates estimated by vegetation indices for the five regions of interest. Foliage periods are the numbers of days
between bud break and abscission. Dates determined visually from images are also shown for bud break, abscission and foliage period.
the GRVI occurred later (Figure 5D). Autumn peaks were
more variable between indices (Figure 5E).
There are essentially three approaches to measuring pheno-
logical changes in vegetation. The first, and most widely
reported, involves direct human observation of dates of
bud break, flowering and leaf fall. Analysis of large data
sets, usually involving several species at a single location
over many years, has enabled generalisations to be made
about the relationship between these phenological stages
and weather patterns (Menzel 2000; Sparks et al. 2000;
Chmielewski and Rötzer 2001). A disadvantage of this
approach is that it relies on a somewhat subjective decision
of the stages by the observers, and can be influenced by
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74 T. Mizunuma et al.
the ‘outliers’, i.e. those individual plants which happen to
be early or late in their phenological behaviour. Moreover,
it is labour intensive, although there appears to be no
shortage of willing volunteers who are able to carry out
daily observations, particularly on garden plants (Hepper
The second approach uses remotely sensed data from
satellites or towers to detect changes in colour (Myneni
et al. 1997; Zhou et al. 2001; Stockli and Vidale
2004). There are disadvantages in this approach too: long
time-series of data are hard to acquire because of the
limited life span of a satellite-borne sensor; in addition,
satellite data are not collected when the landscape is cloud
covered. In some respects it is an advantage that the satel-
lite ‘sees’ a large amount of landscape (spatial resolution is
from a few square metres to many square kilometres), as a
large sample integrates over many thousands of plants, but
species mixtures can affect signal interpretation. Sensors
mounted on flux towers, above the canopy, overcome many
of the objections that apply to satellite data. Such sensors
may be broad-band, like NDVI sensors (Miller et al. 2001),
or they may be hyperspectral radiometers (e.g. Motohka
et al. 2009; Nagai et al. 2010a).
The third approach, described in this paper, is rela-
tively new, and involves digital photography. The approach
has recently acquired popularity because it can be obtained
from public internet-connected cameras (Jacobs et al. 2009;
Ide and Oguma 2010; Graham et al. 2010) and, impor-
tantly, it is associated with the rapidly expanding network of
flux towers (Baldocchi et al. 2005; Richardson et al. 2007;
Wingate et al. 2008; Ahrends et al. 2009). Thus phenol-
ogy can now be linked to photosynthetic and respiratory
CO2exchange, hence providing a deeper understanding of
the controls on the terrestrial carbon cycle. The signals we
present are rich in information, indicating not only the clas-
sical phenological stages but also subtle changes during
the growing season, probably associated with changes in
leaf pigments, leaf area and pathogen infection. Moreover,
the signals from the camera can be stored indefinitely, pro-
viding an archive for subsequent analysis. However, there
remains some confusion about which signals are most
suitable for analysis, and in the current paper we have
attempted to identify the most robust indices. A prime con-
cern is filtering of effects caused by the changing light
conditions, especially those associated with bad weather.
We found that ‘hue’ provided a more robust metric than
those used by most other research groups who based their
indices on simply the strength of the green or red sig-
nal. It showed much less scatter during the summer and a
very good indication of spring bud break (Figure 3). Hue
varied between trees by only 25%, whereas all the other
indices were either much more variable or they did not
change with bud break (Figure 4). It is clear that a sys-
tem such as HSL is theoretically capable of defining any
colour as perceived by the human eye. It can cope with
variations in brightness and tone, which a simple expres-
sion of R, G and B, or a combination of these signals, can
never do. In canopy phenology there are situations of light
and shade, and the haze caused by scattering of short-wave
radiation, which provide much of the information content
perceived by humans. It is clear that hue represents the pure
colour that leaves and flower present, whilst ‘saturation’
and ‘level’ are influenced by non-biological variables such
as radiation flux and direction, and scattering by aerosols.
The use of ‘hue’ has, however, been overlooked in the anal-
ysis of remotely sensed images, in favour of indices such as
NDVI, which was initially developed for satellite sensors
with only limited capability of resolving the electromag-
netic spectrum. Nowadays, with hyperspectral sensors and
colour photography, this traditional approach may not be
There was some disagreement between the phenolog-
ical dates measured by the indices versus purely visual
observation of the images (Figure 5). The bud break dates
assessed by the indices were earlier and the abscission dates
assessed by the indices later than those assessed by visual
observation, and this can have a significant effect on esti-
mates of canopy duration. The trends coincide with the
results of a similar study using green fraction of beech
(Fagus sylvatica L.) trees in two European forests (Ahrends
et al. 2008, 2009). Abscission dates are recognised as being
harder to judge than spring signals even by visual inspec-
tion, as highlighted by Menzel et al. (2006). Presumably
this reflects the complexity of the processes whereby leaves
are shed, as wind and location-on-tree may be important
factors. These inaccuracies in visual assessment may also
partly explain the greater departures of abscission dates
compared with bud break dates of Figure 5.
All indices showed seasonal behaviour. The strength of
the green signal was, not surprisingly, a very reliable indi-
cator of bud break and the abscission of leaves. Hue, despite
its complex and conditional definition (Table 1), was
equally good at estimating canopy duration. Immediately
after bud break, and immediately before abscission, some
of the indices showed very clear maxima. In the case of
the green peak after unfolding, this indicates that all of
the leaves were fully developed. The gradual decline fol-
lowing the peak (in green indices) may be associated with
the maturation of the leaf cuticle and perhaps the pro-
gressive ‘weathering’ and ‘contamination’ of leaf surfaces,
potentially including the colonisation of leaf surfaces by
micro-organisms (Saunders 1971) and possibly the devel-
opment of negative water potentials in summer (Zakaluk
and Sri Ranjan 2008). In crop species, it has been shown
that the decline in green is associated with an increase
in chlorophyll, as a high optical thickness of chlorophyll
makes the leaves appear darker in the green waveband
(Kawashima and Nakatani 1998). In the case of the red peak
before abscission, we presume that the carotenoid pigments
become more apparent as the chlorophyll breaks down (e.g.
Archetti et al. 2009).
Ecological phenomena relating to the canopy, such as
leaf area expansion, leaf shedding, herbivory, disease and
tree health can be seen in these images. Monochrome
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Comparison of colour indices of Fagus images 75
hemispherical images taken from ground-based locations
were commonly used for measuring leaf area index (van
Gardingen et al. 1999). However, monochrome luminance
did not show any evidence of the increase of leaf density
in this study. Although disease and insect damage were not
recorded, it is expected that a decrease in the numbers of
leaves will cause a decline in the green strength and GEI,
and that any change in leaf colour will have an effect on the
Richardson et al. (2009) reported that the change of
white balance on the camera setting from ‘auto’ to ‘fixed’
reduced the day-to-day variability in the green strength.
Although we used fixed white balance (cloudy), the signals
were still noisy even for images captured on only cloudy
days, which were less affected by direct sunlight and precip-
itation. The red and green values were less variable in com-
parison with the blue values. Consequently, hue and GRVI
were not susceptible to weather conditions. It is, however,
interesting to note that the distribution of pixel values for
each ROI showed some systematic differences depending
on the illumination conditions and the season. For exam-
ple, under high illumination conditions, the distributions of
the values of RGB clustered clearly around two separate
distributions for summer and autumn/winter, respectively
(Figure S3, available online). This resulted in very differ-
ent means for the two periods but very similar standard
deviations around the distributions. Images taken under
low illumination conditions appeared more variable season-
ally, both in their means and in their standard deviations,
with some very narrow and some very wide distributions
(Figure S3, available online). Kawashima and Nakatani
(1998) reported a good correlation between chlorophyll
content and the index derived from red and blue signals
for crops in cloudy conditions. However, their reported
condition only applied to a fraction of the growing sea-
son and they did not extend their findings to the rest of
the year. In addition, we think the red- and green-based
indices would be better for a long-term, outdoor observa-
tion programme, because of the vulnerability of the blue
signal to radiation scattered from aerosol optical effects. It
is undeniable that the index without the blue signal loses
not only the optical effects on blue but also the important
colour attribute of leaves. Previous studies (Nakakita 1996;
Graham et al. 2009) claimed the advantage of using hue.
In this study, hue showed robustness in optical conditions
in the vegetative period, which is useful for observing leaf
There are some important lessons to be learned from
the present work before an automated camera system could
be reliably used. First, the use of a standard reference card
in the field of view would be a useful aid for quality con-
trol. The card would have a black and white target area
and also a representation of hues (a colour chart). It might
also incorporate a light sensor, to keep track of changes in
illuminance of the card. Second, it is clear that camera man-
ufacturers are continuously improving the technology of
their product to achieve high-quality photographs, and that
different brands and models of camera may not therefore
produce the same result. Researchers therefore need to co-
operate to share experiences and where possible to make
comparisons of camera and lens types. Third, it is notable
that the result from any one camera depends to a large
extent on the camera settings, and that most ‘convenience’
settings apply a number of corrections. It may be preferable
to disable such corrections, including white balance, so that
consistent data are obtained.
Further work is needed to relate the colour fluctuations
to the pigment system and the physiological activity of the
leaves. It may be some time before colour information may
be used with weather data to reliably estimate canopy pho-
tosynthesis. However, concentrating available cameras at
sites where CO2flux is being measured by eddy covariance
will enable predictive equations to be developed (Nagai
et al. 2010b).
We conclude that the data from this inexpensive digital
camera may be used to detect phenological events with ade-
quate precision, and that other more gradual events which
elude the human observer are also detected by the camera.
Further work will be required to relate these changes to the
pigment concentrations in the canopy, and ultimately to the
physiological activity. We propose that of all the candidate
colour indices, hue is the most promising for the detec-
tion of bud break as it was less affected by atmospheric
conditions (e.g. for distant trees seen through bluish haze).
This work was supported by the UK–Japan 2008 Collaborative
Project Grant Award of the British Embassy, Tokyo and the
British Council to commemorate the 150th anniversary of
official diplomatic relations between Japan and the UK, and
KAKENHI (19688012; Grant-in-Aid for Young Scientists (A))
of the Japan Society for the Promotion of Science. Salary
for TM was made available through a Jim Gray Seed Trust
awarded to LW by Microsoft Research. LW was supported by
the Natural Environment Research Council Advanced Fellowship
award NE/G014418/1. Yousei Hayashi is thanked for the mete-
orological data collected at Mt. Tsukuba. We thank Andrew
Richardson and his team for allowing us to use their MATLAB
code. We are indebted to the Phenological Eyes Network (PEN)
for their support with the camera systems.
Notes on contributors
Toshie Mizunuma is a Ph.D. student at the University of
Edinburgh, studying the seasonal change of forest canopy and
their relevance for the global carbon cycle. She received an M.Sc.
in Forest GeoSciences from the University of Edinburgh and an
M.Sc. in Environmental Sciences from the University of East
Anglia. Her main research interests are phenology of plants in
boreal and temperate ecosystems and the observation methods
using optical equipment.
Tomokazu Koyanagi participated in this research as a Masters
student in the Institute of Agricultural and Forest Engineering,
University of Tsukuba with main interests in vegetation changes
in East Asia using remote sensing.
Maurizio Mencuccini is a Reader in the School of Geosciences
at the University of Edinburgh. His research interests include
net ecosystem productivity of forest ecosystems; functional
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76 T. Mizunuma et al.
implications of architectural scaling in plants; tree–water rela-
tions; carbon stocks and fluxes; tree development and ageing;
Kenlo Nishida Nasahara is an Associate Professor, in the
Institute of Agricultural and Forest Engineering, University of
Tsukuba. His research interests focus on remote sensing of ter-
restrial ecosystems in relation to evapotranspiration and carbon
Lisa Wingate is a Natural Environment Research Council
Advanced Fellow in the Plant Sciences Department at the
University of Cambridge. Her research interests include under-
standing the response of tree growth to changes in climate,
constraining photosynthesis and respiration at large scales using
the carbon and oxygen isotope composition of CO2exchanged
between ecosystems and the atmosphere, and other ‘forest synthe-
sis’ activities, including linking phenology to plant function and
carbon allocation using webcam networks.
John Grace is Emeritus Professor of Environmental Biology,
in the School of GeoSciences at the University of Edinburgh.
His research interests include plant ecophysiology, atmosphere–
biosphere interactions and the long-term effects of global change
on ecosystem functioning, including carbon sequestration by
terrestrial ecosystems.
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... Different vegetation indices can be extracted for different geographical regions from digital images. For different ecosystems and illumination conditions, the indices vary in their performance on characterizing phenology events [1][2][3][4][5][6][7][8][9][10][11][12][13]. Correlation of the vegetation indices with other phenological data from other sensors has been studied [1,3,[5][6][7]11,14]. Usage of vegetation indices to study ecological indicators, e.g., the nutrition of wild animals, has also been investigated [4,9,15,16]. ...
... For different ecosystems and illumination conditions, the indices vary in their performance on characterizing phenology events [1][2][3][4][5][6][7][8][9][10][11][12][13]. Correlation of the vegetation indices with other phenological data from other sensors has been studied [1,3,[5][6][7]11,14]. Usage of vegetation indices to study ecological indicators, e.g., the nutrition of wild animals, has also been investigated [4,9,15,16]. ...
... where R, G and B are the digital numbers for the red, green and blue channel [7,11,13,14]. Brightness and luminance values are normalized to values between 0 and 1 in the analysis. Time series of color fraction indices from webcam images can be used in the determination of phenological events. ...
Full-text available
A system for multiple camera networks is proposed for continuous monitoring of ecosystems by processing image time series. The system is built around the Finnish Meteorological Image PROcessing Toolbox (FMIPROT), which includes data acquisition, processing and visualization from multiple camera networks. The toolbox has a user-friendly graphical user interface (GUI) for which only minimal computer knowledge and skills are required to use it. Images from camera networks are acquired and handled automatically according to the common communication protocols, e.g., File Transfer Protocol (FTP). Processing features include GUI based selection of the region of interest (ROI), automatic analysis chain, extraction of ROI based indices such as the green fraction index (GF), red fraction index (RF), blue fraction index (BF), green-red vegetation index (GRVI), and green excess (GEI) index, as well as a custom index defined by a user-provided mathematical formula. Analysis results are visualized on interactive plots both on the GUI and hypertext markup language (HTML) reports. The users can implement their own developed algorithms to extract information from digital image series for any purpose. The toolbox can also be run in non-GUI mode, which allows running series of analyses in servers unattended and scheduled. The system is demonstrated using an environmental camera network in Finland.
... They are not affected by cloud conditions as is satellite imagery (Polgar and Primack 2011), but may be affected by understorey greening (Inoue et al. 2014). Sideways-facing cameras, as used in the Phenocams network in the USA (Richardson et al. 2007) and the Phenological Eyes Network in Japan (Inoue et al. 2014), are less influenced by the understorey, though image quality can be affected by light conditions (Mizunuma et al. 2011). With both types of imagery, it is possible, though sometimes difficult, to isolate trees so that the time series of individual tree canopy development can be derived (Inoue et al. 2014;Polgar and Primack 2011). ...
... The greenness index data was comparable to visual observations in terms of 50% DOY but not rate. Previous studies using fixed cameras on canopy towers found greenness to be closely related to leaf expansion, though in one study, greenness identified earlier 50% DOY than visual observations (Mizunuma et al. 2011). Greenness is a function of both leaf expansion and pigment changes, so while related, leaf expansion and greenness are different (Keenan et al. 2014). ...
Phenological variation in spring leafing between and within species can determine plant responses to warmer winter and spring temperatures in the short term. Methods are needed for monitoring canopy development that can be replicated on a large-scale, while retaining fine-scale resolution at the level of individual trees. Citizen science has the potential to provide this, but a range of approaches exist in terms of the phenophase recorded (e.g. budburst or leaf expansion), how the phenophase is characterised (first events or intensity monitoring) and the portion of tree crown assessed and observation frequency. A comparison of spring budburst and leaf expansion of four tree species (Fraxinus excelsior, Fagus sylvatica, Quercus robur and Acer pseudoplatanus) was monitored in one woodland using (1) counts of expanded leaves on three crown sections, (2) percentage estimates of expanded leaves across the whole crown and (3) a greenness index from photography. Logistic growth models were applied to make comparisons. First-event dates were found to be misleading due to high variation in leaf development rates within and between species. Percentage estimates and counts produced similar estimates of leaf expansion timing and rate. The greenness index produced similar estimates of timing, but not rate, and was compromised by practicalities of photographing individual crowns in closed-canopy woodland. Citizen scientists could collect data across the period of spring leafing, with visual counts and/or estimates made every 3–4 days, subject to tests of reliability in pilot citizen science studies.
... With ongoing leaf senescence, Sblue increased rapidly, causing a decrease in Sgreen. The comparison of several colour indices showed that the HUE value provided a more robust metric than the other indices (Mizunuma et al., 2011(Mizunuma et al., , 2014, while the redness index was better for estimating leaf senescence and the greenness index was better for estimating leaf development events (Zhao et al., 2012). In this study site, the Sred and HUE values did not show an evident pattern associated with the phenological events. ...
Vegetation phenology is an important indicator of environmental change and strongly connected to forest ecosystem productivity change. This study aimed to analyse the pattern of phenological variations derived from digital imagery for the interpretation of ecosystem productivity. For 2014, 2015 and 2016, the seasonal phenological development of savanna was analysed by using towerbased imagery from a digital camera. The green excess index (GEI) was the best at representing the phenological transition dates (PTDs) and useful for investigating the gross primary production (GPP) in the savanna ecosystem. There was a significant correlation between the monthly pattern of the strength of green (Sgreen), green excess index (GEI) and vegetation contrast index (VCI) and GPP throughout the year. Additionally, the annual pattern of colour indices had significant relationship (p < 0.05) with GPP but this was not seasonal. The air temperature (air T) and soil temperature (soil T) were strongly significantly correlated (p < 0.001) with the start of growing season (SGS) and caused the advance in green-up and the timing of the start of the growing season in 2014 and 2016. The short growing season length (GSL) had an impact on the productivity. The colour indices from the digital camera images not only provided the phenological pattern of a forest canopy but also revealed the forest ecosystem productivity by showing the response to environmental factors. Our results indicate that daily continuous digital camera images might be useful for ecologists to use as a tool for future prediction of the long-term phenological modelling.
... The hue value h was used for leaf color comparison, since it has been previously described as a robust measure for leaf color (Mizunuma et al., 2011). ...
Full-text available
Modern plant cultivars often possess superior growth characteristics, but within a limited range of environmental conditions. Due to climate change, crops will be exposed to distressing abiotic conditions more often in the future, out of which heat stress is used as example for this study. To support identification of tolerant germplasm and advance screening techniques by a novel multivariate evaluation method, a diversity panel of 14 tomato genotypes, comprising Mediterranean landraces of Solanum lycopersicum, the cultivar “Moneymaker” and Solanum pennellii LA0716, which served as internal references, was assessed toward their tolerance against long-term heat stress. After 5 weeks of growth, young tomato plants were exposed to either control (22/18°C) or heat stress (35/25°C) conditions for 2 weeks. Within this period, water consumption, leaf angles and leaf color were determined. Additionally, gas exchange and leaf temperature were investigated. Finally, biomass traits were recorded. The resulting multivariate dataset on phenotypic plasticity was evaluated to test the hypothesis, that more tolerant genotypes have less affected phenotypes upon stress adaptation. For this, a cluster-analysis-based approach was developed that involved a principal component analysis (PCA), dimension reduction and determination of Euclidean distances. These distances served as measure for the phenotypic plasticity upon heat stress. Statistical evaluation allowed the identification and classification of homogeneous groups consisting each of four putative more or less heat stress tolerant genotypes. The resulting classification of the internal references as “tolerant” highlights the applicability of our proposed tolerance assessment model. PCA factor analysis on principal components 1–3 which covered 76.7% of variance within the phenotypic data, suggested that some laborious measure such as the gas exchange might be replaced with the determination of leaf temperature in larger heat stress screenings. Hence, the overall advantage of the presented method is rooted in its suitability of both, planning and executing screenings for abiotic stress tolerance using multivariate phenotypic data to overcome the challenge of identifying abiotic stress tolerant plants from existing germplasms and promote sustainable agriculture for the future.
... The remarkable changes in ρ green and τ green caused the unique seasonal patterns of canopy reflectance in the temperate deciduous forest. Several studies have reported that deciduous forest canopies in temperate regions show clearer seasonal changes in reflectance in green than in blue and red bands (e.g., Richardson et al., 2007;Mizunuma et al., 2011;Nagai et al., 2014). In those forests, dominant canopy trees flush and expand their leaves simultaneously. ...
Leaf optical properties (reflectance and transmittance spectra of a single leaf) are the key to interpreting remote sensing data regarding the structure and functions of vegetation canopies. These properties are determined by leaf biochemical and anatomical traits, which change seasonally in deciduous species. To reveal the species-specific seasonal patterns in leaf optical properties and their relationship to chlorophyll content and/or mesophyll structure in deciduous trees, we examined these mechanistic relationships in a pioneer species, Betula ermanii, and in sunlit and shade leaves of a late-successional species, Quercus crispula, during their seasonal development. Leaf optical properties were measured from leaf emergence to leaf fall in four years. After leaf unfolding, reflectance in the photosynthetically active radiation (PAR) region decreased gradually, while transmittance dropped rapidly. Both reflectance and transmittance increased before leaf fall. In the near-infrared (NIR) region, reflectance increased and transmittance decreased during the development period. Values of parameter N (the number of compact layers within a leaf in PROSPECT-5 model) in young leaves was very low (1.1 for B. ermanii and 1.0 for sunlit and shade leaves of Q. crispula) but increased rapidly by 30% for B. ermanii and sunlit leaves of Q. crispula and by 20% for shade leaves. N was higher in the order of B. ermanii > Q. crispula sun leaves > shade leaves. The PROSPECT-5 simulation with our measured data showed that the development of mesophyll tissue increases reflectance and decreases transmittance in both NIR and PAR regions, and an increase in chlorophyll decreases both reflectance and transmittance in PAR region. Our results indicate that structural and biochemical development of leaf mesophyll and its interspecific variations are essential biological characteristics for understanding seasonal changes in canopy reflectance. The results of this study will help us to better analyze remotely sensed data.
... The network has cameras in 37 sites, which are in different countries but mostly in Japan. Numerous studies are conducted using image data from PEN [13,27,28]. Australian Phenocam Network has cameras providing images from volunteering research sites in Australia[29, 30,31]. ...
... Third, only one color index (i.e., green chromatic coordinates) was calculated in current analyses to examine seasonal phenology. Other RGB-based color indices (e.g., green excess index, green red vegetation index [62]) might be also considered to characterize the uncertainty of seasonal phenology. Fourth, RGB-based imagery with three broad-band spectral signals might not have enough information to accurately characterize plant dynamics in particular for seasonal phenology, and future application of numerous narrow-band vegetation indices from multi-or hyper-spectral imagery has the potential to improve the analyses of such plant community dynamics [63,64]. ...
Full-text available
Rapid invasion of Spartina alterniflora into Chinese coastal wetlands has attracted much attention. Many field and remote sensing studies have examined the spatio-temporal dynamics of S. alterniflora invasion; however, spatially explicit quantitative analyses of S. alterniflora invasion and its underlying mechanisms at both patch and landscape scales are seldom reported. To fill this knowledge gap, we integrated multi-temporal unmanned aerial vehicle (UAV) imagery, light detection and ranging (LiDAR)-derived elevation data, and tidal and meteorological time series to explore the growth potential (lateral expansion rates and canopy greenness) of S. alterniflora over the intertidal zone in a subtropical coastal wetland (Zhangjiang estuarine wetland, Fujian, China). Our analyses of patch expansion indicated that isolated S. alterniflora patches in this wetland experienced high lateral expansion over the past several years (averaged at 4.28 m/year in patch diameter during 2014–2017), and lateral expansion rates ( y , m/year) showed a statistically significant declining trend with increasing inundation ( x , h/day; 3 ≤ x ≤ 18 ): y = − 0.17 x + 5.91 , R 2 = 0.78 . Our analyses of canopy greenness showed that the seasonality of the growth potential of S. alterniflora was driven by temperature (Pearson correlation coefficient r = 0.76 ) and precipitation ( r = 0.68 ), with the growth potential peaking in early/middle summer with high temperature and adequate precipitation. Together, we concluded that the growth potential of S. alterniflora was co-regulated by tidal and meteorological regimes, in which spatial heterogeneity is controlled by tidal inundation while temporal variation is controlled by both temperature and precipitation. To the best of our knowledge, this is the first spatially explicit quantitative study to examine the influences of tidal and meteorological regimes on both spatial heterogeneity (over the intertidal zone) and temporal variation (intra- and inter-annual) of S. alterniflora at both patch and landscape scales. These findings could serve critical empirical evidence to help answer how coastal salt marshes respond to climate change and assess the vulnerability and resilience of coastal salt marshes to rising sea level. Our UAV-based methodology could be applied to many types of plant community distributions.
... We extracted digital RGB data from the ROI of the images and calculated the strength of each RGB colour channel (S red , S green , S blue ) and Hue (equations are listed in Table 1). The strength of a channel is the ratio of the digital values of each channel to the total digital value of all RGB channels, and Hue is one of the dimensions of the Hue Saturation Lightness (HSL) colour scheme (Mizunuma et al., 2011). ...
The widespread increase in the number of digital cameras mounted on flux towers provides an opportunity to better understand the relationship between the seasonality of canopy photosynthesis and canopy phenology. The challenge is due to fewer variations in rubber defoliation of rubber canopy. We examined the relationship between colour indices calculated from digital camera images and gross primary production (GPP) obtained from daily flux tower observations of carbon dioxide over two years in a rubber plantation and used these colour indices to model GPP. According to the results, (1) the strength of green (S green), Hue and GPP exhibited clear seasonal patterns in the rubber plantation, and the relationship between the camera-based indices and GPP appeared to have distinct characteristics at different phenological stages; (2) the peak GPP from the flux tower measurements lagged behind the peak in the colour indices calculated from digital camera imagery; (3) GPP was strongly correlated with S green derived from camera imagery in the rubber plantation, especially in the leaf expansion period; and (4) the GPP simulated by colour indices (S green and Hue) was underestimated, and the fraction of absorbed photosynthetically active radiation (FPAR) was the best parameter for modelling GPP in normal years. Our results indicate that colour indices calculated from digital camera images can be used to model GPP in rubber plantations and to monitor biotic and abiotic stress events. Future research should measure the pigment contents of canopy leaves to precisely quantify the relationship between colour indices and GPP.
... El avance en el desarrollo de las cámaras digitales RGB estándar (Ahrends et al., 2008) impulsó, de manera simultánea, el desarrollo de software para procesamiento y análisis de imágenes digitales (Ahrends et al., 2008;Mizunuma et al., 2011;Sakamoto et al., 2012). Las cámaras digitales actuales pueden considerarse sensores cercanos de teledetección de alta resolución espacial y temporal, poseen un sistema cartesiano de captura de colores en el espacio rojo (R), verde (G) y azul (B), donde los números digitales RGB (DN) posibilitan registrar eventos de la vegetación (Gitelson et al., 2002;Sonnetag et al., 2011;Sakamoto et al., 2012). ...
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Abstract The aerial forage biomass productivity (AFBP) of wheat varieties (Triticum aestivum L.) was quantified and compared using ANAVA in a facility located at Colonia El Simbolar, Santiago del Estero, Argentina. AFBP of the varieties were related to digital camera-derived chromatic coordinates and from the data lineal models were adjusted. Significant differences in the accumulated AFBP (which ranged from 4.4 and 5.6 tn ms ha-1) among varieties were not found. On the contrary, significant differences were found in the AFBP of the third (R2 = 0.55; p<0.01) and fourth (R2 = 0.47; p<0.01) cut, being Yarara (2.1 tn ms ha-1) and Gladiator (1.1 tn ms ha-1 ) varieties those with the highest productivity: Strong inverse (r -0.99; r -0.88) and straight (r 0.96) relationships were found among the AFBP of the varieties and the integrate chromatic coordinate Σcct. It is highlighted that the simple chromatic coordinate Σcct (red) showed strong inverse relationship with the AFPB of the varieties Yarara and Gladiator with a r -0.99 for both varieties. The lineal models adjusted by the AFPB and the chromatic coordinate presented a high explanatory power with a R2Aj ranging between 0.71 and 0.99. The digital camera-derived chromatic coordinates allow for the aerial forage biomass productivity of wheat varieties to be quantified easily and economically. Key words: RGB, near sensor, lineal models, chromaticity.
Autumn leaf phenology and its color brightness provide valuable information for managing forest carbon cycles and cultural ecosystem services. Digital repeat photography has provided standard phenological data, but the methodologies for detecting autumn leaf coloring have various strengths and weaknesses. We assessed the accuracy, sensitivity, and uncertainty of various model and color index combinations for detecting autumn leaf coloring. Then we identified the most robust and sensitive methods, using digital repeat photography data from Japanese alpine vegetation. For determining autumn leaf color duration, quadratic or multinomial discriminant analysis using RGB digital numbers had the highest accuracy (hit ratio > 0.7). For determining the peak day of autumn leaf color and its color brightness, we compared uncertainty of methodologies by randomly resampling 80% of the data 20 times to mimic observation errors (e.g., due to heavy rain). The spline-fitted red/green reflectance ratio (RGR) and visible atmospherically resistant index (VARI) proved robust for detecting the peak day (median SD = 1.25). Uncertainty of color brightness was also low when using VARI fitted by a double logistic model for both red and yellow leaves (median coefficient of variation = 1.03). These two indexes are stable despite atmospheric effects, which may result in robustness to daily variation in conditions (e.g., fog). We compared sensitivity of leaf color brightness: RGR and excess red (ExR) fitted by a double logistic model had the highest sensitivity to red and yellow leaves exceeding the average of other combinations by 26% and 88% in median values, respectively. The small denominator or lack of a denominator of these indexes increases the sensitivity to red or yellow. Our results demonstrate the averaged accuracy, sensitivity, and robustness of each methodology among our research sites with different camera observations. These methods should help in utilizing hidden big data from web cameras or past photos that were not intended for scientific research to properly assess autumn leaf phenology and its color brightness.
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the purpose of understanding pat-terns and processes controlling carbon budgets across a broad range of scales, explicit activities to assess the impact of phenol-ogy on ecosystem carbon bal-ance are still somewhat lacking within the carbon cycle commu-nity. The reasons are clear: long-term observations, otherwise called 'monitoring' are not popu-lar with those that sponsor re-search in this area; three or five year projects are the norm, when in practice much longer records are required to detect long-term trends and their rela-tionships to climatic drivers. There is however, evidence for a shift in attitudes. Keeling's meas-urements of atmospheric CO 2 concentrations, that began in 1958, are an outstanding exam-ple of the value long-term moni-toring represents in the context of a changing world (Nisbet, 2007). Moreover, continuous eddy covariance measurements of CO 2 fluxes began in the early 1990s at a handful of sites. Every year, more and more sites have been added to FLUXNET, and many of these are now providing useful long term data not only with regard to spatial patterns of carbon uptake and release, but also in relation to the influence of phenology on carbon seques-tration. One example of a synergy be-tween phenology and flux moni-toring networks in Europe has Why observe phenology within FLUXNET? Phenology is the study of the timing of lifecycle events, espe-cially as influenced by the sea-sons and by the changes in weather patterns from year to year. The oldest phenological records, observations of cherry flowering at the Royal Court in Kyoto date back to 705 AD, and are still maintained to this day across Japan where the Japanese Meteorological Agency use these data to provide weekly forecast maps of expected blooming dates (http: Marsham, the father of modern phenological recording, was a wealthy landowner and amateur naturalist who recorded "Indications of spring" in Nor-folk, England, beginning in 1736. His family maintained these re-cords until the 1950s. In the modern era, phenology has gained a new impetus, as people realize that such records, if sus-tained over many years, can reveal how plants and animals respond to climate change. Moreover, phenological events such as the spring leaf-out and the autumn fall exert a strong control on both spatial and tem-poral patterns of the carbon cycle. Phenology also influences hydrologic processes, as spring leaf-out is accompanied by a marked increase in evapotranspi-ration, and nutrient cycling as autumn senescence results in a flush of fresh litter (nutrient) input to the forest floor. Phenology is a robust integrator of the effects of climate change on natural systems (Schwartz et al., 2006; IPCC 2007), and it is recognized that improved moni-toring of phenology on local-to-continental scales is needed. Historically, phenological obser-vations were a pastime of ama-teur naturalists (e.g. the Mar-sham family) and reliable records were often dependent on the skills and effort of the observer. The increased demand for inter-national co-operation and stan-dardisation in this area led to the creation of many large-scale phenological monitoring net-works such as the International Phenology Garden (IPG) pro-gram (http: (established in 1998) as well as the recently-established USA-National Phenology Network (U S A -N P N) ( and associated regional networks (e.g., These networks have focused on developing standardized proto-cols for phenological observa-tions, and ensuring overlap be-tween plant species found across locations. Although there are obvious advantages in creating explicit linkages between these
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Variations in the amplitude and timing of the seasonal cycle of atmospheric CO2 have shown an association with surface air temperature consistent with the hypothesis that warmer temperatures have promoted increases in plant growth during summer1 and/or plant respiration during winter2 in the northern high latitudes. Here we present evidence from satellite data that the photosynthetic activity of terrestrial vegetation increased from 1981 to 1991 in a manner that is suggestive of an increase in plant growth associated with a lengthening of the active growing season. The regions exhibiting the greatest increase lie between 45°N and 70°N, where marked warming has occurred in the spring time3 due to an early disappearance of snow4. The satellite data are concordant with an increase in the amplitude of the seasonal cycle of atmospheric carbon dioxide exceeding 20% since the early 1970s, and an advance of up to seven days in the timing of the drawdown of CO2 in spring and early summer1. Thus, both the satellite data and the CO2 record indicate that the global carbon cycle has responded to interannual fluctuations in surface air temperature which, although small at the global scale, are regionally highly significant.
Phenological eyes network (PEN) is designed as a validation campaign of satellite remote sensing data for terrestrial ecosystem observation. This project has started in several carbon-flux observatories of AsiaFlux projects. Main part of PEN consists of three core instruments : Automatic-capturing Digital Fisheye Camera (ADFC), HemiSpherical Spectro-Radiometer (HSSR) and SunPhotometer (SP). ADFC serves images mainly for a phenological study, sky condition and leaf area index (LAI). HSSR provides vegetation spectral parameters and a photosynthetically active radiation. SP provides atmospheric parameters for an atmospheric correction of satellite images. In combination with these PEN observations and flux or ecosystem research, validation study of ecosystem remote sensing will be enhanced in various aspects, namely, radiative quality check as well as ecological standpoints. PEN is an open project and welcomes involvements from various groups.
Phenology is the study of reoccurring life-cycle events that are initiated and driven by environmental factors. It is an inter-disciplinary and integrative field that presents some key cross-cutting challenges. However, some recent advances in data and technology can help rise to these challenges. With these advances, phenology is poised to help address some critical ecological issues. This article presents an overview of phenology and some of its cross-cutting challenges. It then reviews some advances being made in the field and how these connect to land management, climate change, and climate modeling studies.
In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.