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Yuxia Liu Phenology 2018 poster on tracking grass phenology

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

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Yuxia Liu Phenology 2018 poster on tracking grass phenology

  1. 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 WeChat ID: niuer1992223

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