University of Technology Sydney Yuxia Liu's Phenology 2018 conference poster on tracking grass phenology with phenocams and remote sensing over victorian pastures.

1. Tracking Grass Phenology with Phenocams and
Remote Sensing over Victorian Pastures
Liu, Y.1*, Huete, A. 1, Xie, Q.1, Nguyen, H. 1, Grant, I. 2, Ebert, E. 2
The seasonal progression of periodic biological occurrences in plants is generally referred to as vegetation phenology. Flowering, pollination and pollen release are important phenology
stages of the grass life cycle and grass pollen is a major trigger for aeroallergens and is among the highest in Australia. To better understand this complex ecological-human health interplay,
a set of time-lapse digital RGB phenocams, were deployed over 4 grass pasture areas in the state of Victoria. Within and cross site variations in grass phenology were analysed through
computed green chromatic coordinate (GCC) time series. Enhanced Vegetation Index (EVI) time series were computed from MODIS Vegetation Indices product and Sentinel-2 level-2A
surface reflectance product to detect satellite-derived phenological variations. Our objective was to (1) investigate the utility of phenocams for monitoring grassland phenology including
greening, flowering and curing, as well as (2) demonstrate the potential of phenocams to validate satellite-derived phenology. Significant variations in the GCC profiles were found in terms
of greenness amplitude, greenness peaks, flowering, and curing. Visual phenocam assessments of grass flowering were found to be coincident over a range of peak greenness and curing
phenophase stages. Proximal phenocam GCC results were found to be in good agreement with 10 m satellite data from Sentinel-2 and the commonly-used MODIS. Our results
demonstrate the potential of phenocams for proximal monitoring of grass phenology, as well as to validate satellite-derived phenology, and thus contribute to the development of more
accurate pollen forecast models.
Methods
Df
Results
Conclusion
• Four grass pasture sites, coupled to four pollen
traps, north and west of Melbourne in the state of
Victoria, were studied (Fig. 1).
Remote Sensing
Fig. 1 Location of phenocams and pollen traps in Victoria
GCC = GDN/(RDN + GDN + BDN)
Fig. 4 ROIs at four research sites
Phenocam 1. Analysing variations in greenness of pasture and flowering time using phenocam
Fig. 3 GCC profiles at 4 sites, the date of GCC peak and flowering
Table 1 The Max/Min GCC values,
amplitude of GCC profiles,
and flowering time at 4 sites
Abstract
2. Comparison of MODIS/Sentinel-2 EVI and phenocam GCC
• MODIS Enhanced Vegetation Index (EVI)
(MOD13Q1, 250m) and Sentinel-2 Level-2A
surface reflectances (10m) were used,
• MODIS EVI- the data was shifted 8-day to
more accurately align with phenocam data.
MODIS single pixel (250m) extracted,
centered on the phenocam site,
• Sentinel-2- EVI values were computed &
averaged to different spatial windows (10m,
30m, 250m, 1km and 3km), centered on the
phenocam site, where EVI is computed as,
Phenocam MOD13Q1 Sentinel-2 L2A
EVIFlowering GCC
Can phenocams detect grass
greenness phenology, curing,
and timing of flowering
accurately?
Demonstrate the potential of phenocams
for proximal monitoring of grass phenology,
as well as to validate satellite derived
phenology.
Fig. 2 Flow chat and analysis strategy
• A pair of RGB phenocams were deployed at each site,
from 26 Sep. to 31 Dec. 2017.
• To trace phenological status of pastures over time,
green chromatic coordinate (GCC) values for a region
of interest (ROI) (Fig. 4) were calculated for each
image , where GCC is defined as
Fig. 5 Timeline of flowering and visible phenology events
• Fig. 6 illustrates the variations of Sentinel-2 and MODIS EVI
are significantly consistent with GCC. Further, Sentinel-2 EVI
profiles correlate better with GCC than MODIS EVI profiles.
• For MODIS EVI profiles, there are declines corresponding with
the GCC peak at Casterton, highlighting a crucial MODIS
weakness. There was also no Sentinel-2 data available over
this key period, an important weakness of current Sentinel-2
data. At Kyabram, the MODIS EVI also experienced a sudden
increase during the curing period, which may be an artifact
associated with a bad QA pixel .
• Overall, one of the most useful findings of this study is that
proximal phenocam GCC results are in good agreement with
10m satellite data from Sentinel-2. Fig. 7 shows all EVIs at
different spatial scales were significantly correlated with
phenocam GCC at Kyabram and Redesdale. This demonstrates
that Sentinel-2 allows retrieval and analysis of pasture
phenology with high accuracy.
• Flowering shows a relatively complex
pattern with greenness peak, with
the flowering times varying across the different sites from 13 Oct. to 13 Nov. The main reason
for these trends might be variations in types of grassland. However, overall flowering activity
was more pronounced during grassland curing.
Fig. 7 Regression between Sentinel-2 EVIs and phenocam GCC
• Flowering in Casterton first appeared
on 13 Nov. (317 DOY) while overall
grass greenness declined. After that,
flowering continued until end of the
measurements. With peak GCC on 23
Oct. (296 DOY), the flowering time was
21 days lag from greenness peak.
• For Kyabram, the flowers had appeared
already when we started observations,
however, the flowers became obvious
as greenness declined, i.e. 19 Oct. (292
DOY). The peak of greenness could be
estimated on 27 Sep. (270 DOY) and
the flowering time is 22 days lag from
the greenness peak.
• For Redesdale, the appearance of
flowers corresponded with increasing
greenness near 13 Oct. (286 DOY). The
peak greenness was on 16 Oct. (289
DOY), with flowering time 3 days
advanced from greenness peak.
However, most flowers appeared in the
images during greenness declines.
Objectives
Conclusions
• Can phenocam detect grass greenness phenology, curing, and timing of flowering accurately?
• Demonstrate the potential of phenocams for proximal monitoring of grass phenology, as well
as to validate satellite derived phenology.
• Further study is needed to ensure phenocam capability to quantify flowering timing.
• Our results demonstrate the potential of phenocams for proximal monitoring of grass
phenology, as well as to validate satellite derived phenology, and thus contribute to the
development of more accurate pollen forecast models.
1. Ecosystem Dynamics, Health and Resilience, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Sydney Australia
2. Bureau of Meteorology, Melbourne, Australia
Contact* Yuxia Liu, PhD. Candidate Email: yuxia.liu@student.uts.edu.au
• Significant variations in the
GCC profiles were found in
terms of greenness,
amplitude, greenness peaks
and curing (Fig. 3)
• The biggest amplitude of
GCC profiles belong to Mount
Gellibrand, and the GCC
profiles in Casterton and
Redesdale have relative
gentle patterns. The dates of
GCC peak are around 20 Oct.
• The Min GCC at Casterton can not be determined
because of shortage of observations, and the Max
& Min GCC only could be estimated at Kyabram
and Redesdale, respectively.
EVI =2.5 x ((NIR - Red)/(NIR + 6 x Red - 7.5 x Blue + 1))
Fig. 6 Comparison the variation trends of MODIS/Sentinel-2 EVI and phenocam GCC
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