Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools

Huete, A; Tran, NN; Nguyen, H; Xie, Q; Katelaris, C

Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools

Huete, A

Tran, NN Nguyen, H

Xie, Q

Katelaris, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 5429 - 5432
Issue Date:: 2019-07

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Field	Value	Language
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376	en_US
dc.contributor.author	Tran, NN	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0002-8329-1362	en_US
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610	en_US
dc.contributor.author	Katelaris, C	en_US
dc.date	2019-07-28	en_US
dc.date.accessioned	2020-06-11T06:51:09Z
dc.date.accessioned	2020-06-11T06:51:10Z
dc.date.available	2020-06-11T06:51:09Z
dc.date.available	2020-06-11T06:51:10Z
dc.date.issued	2019-07	en_US
dc.identifier.citation	IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 5429 - 5432	en_US
dc.identifier.isbn	978-1-5386-9154-0	en_US
dc.identifier.issn	2153-6996	en_US
dc.identifier.uri	http://hdl.handle.net/10453/141339
dc.description.abstract	Grass pollens are a major source of aeroallergens globally, inducing allergic asthma and hay fever in up to 500 million people worldwide. Pollen forecasting research and methods are site-dependent and tend to be empirically derived composites of expert knowledge and weather data. In this study we utilize satellite-based information of landscape conditions and phenology to better discern and predict grass pollen evolution. We employed machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering-induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium	en_US
dc.relation.ispartof	IEEE International Geoscience and Remote Sensing Symposium	en_US
dc.relation.isbasedon	10.1109/igarss.2019.8898796	en_US
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Yokohama, Japan	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false	en_US
pubs.finish-date	2019-08-02	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2019-07-28	en_US

Abstract:

Grass pollens are a major source of aeroallergens globally, inducing allergic asthma and hay fever in up to 500 million people worldwide. Pollen forecasting research and methods are site-dependent and tend to be empirically derived composites of expert knowledge and weather data. In this study we utilize satellite-based information of landscape conditions and phenology to better discern and predict grass pollen evolution. We employed machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering-induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141339