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Satellite tracking of emerging health threats from grass pollen exposure (old title was: Ecological and climate drivers of changing grass pollen aerobiology)

Funding: 2017: $196,500
2018: $169,000
2019: $157,000

Project Member(s): Huete, A.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)

Start year: 2017

Summary: Grass pollens are the key environmental allergen source in Australia and the primary cause of allergic diseases. Pollen exposure has dramatically increased since the 1960's and we don't know why. This Project will investigate and attribute the ecological causes of changing pollen allergen exposures through an innovative integration of 40 years of satellite data, field phenology cameras, and multiple pollen traps that will track grass pollen sources, their evolution, and impact areas. The outcomes will advance our knowledge of environmental drivers and enable more accurate pollen forecasts that will help alleviate the medical and socioeconomic burden costs of allergic diseases, which is estimated at 30 billion dollars.

Publications:

Ma, X, Huete, A & Tran, NN 2019, 'Interaction of seasonal sun-angle and savanna phenology observed and modelled using MODIS', Remote Sensing, vol. 11, no. 12.
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Newlands, NK, Porcelli, TA, Potgieter, AB, Kouadio, L, Huete, A & Guo, W 2019, 'Editorial: Building and delivering real-world, integrated sustainability solutions: Insights, methods and case-study applications', Frontiers in Environmental Science, vol. 7, no. May.
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Wang, S, Ju, W, PeƱuelas, J, Cescatti, A, Zhou, Y, Fu, Y, Huete, A, Liu, M & Zhang, Y 2019, 'Urban-rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons.', Nature Ecology and Evolution, vol. 3, no. 7, pp. 1076-1085.
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Watson, CJ, Restrepo-Coupe, N & Huete, AR 2019, 'Multi-scale phenology of temperate grasslands: Improving monitoring and management with near-surface phenocams', Frontiers in Environmental Science, vol. 7, no. FEB.
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Teluguntla, P, Thenkabail, PS, Oliphant, A, Xiong, J, Gumma, MK, Congalton, RG, Yadav, K & Huete, A 2018, 'A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform', ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, vol. 144, pp. 325-340.
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Keywords: Satellites, remote sensing, grass, pollen, phenology, landscape ecology

FOR Codes: Landscape Ecology, Photogrammetry and Remote Sensing, Ecological Impacts of Climate Change, Environmental Health, Health Protection and/or Disaster Response, Flora, Fauna and Biodiversity at Regional or Larger Scales