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

Alfredo Huete; Ngoc Nguyen Tran; Ha Nguyen; Qiaoyun Xie; Constance Katelaris

doi:10.1109/IGARSS.2019.8898796

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

Alfredo Huete, Ngoc Nguyen Tran, Ha Nguyen, Qiaoyun Xie, Constance Katelaris

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	5429-5432
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898796
Publication status	Published - Jul 2019
Externally published	Yes
Event	2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Conference

Conference	2019 IEEE International Geoscience and Remote Sensing Symposium
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Access to Document

10.1109/IGARSS.2019.8898796

Cite this

Huete, A., Tran, N. N., Nguyen, H., Xie, Q., & Katelaris, C. (2019). Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 5429-5432). Article 8898796 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IGARSS.2019.8898796

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

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.",

author = "Alfredo Huete and Tran, {Ngoc Nguyen} and Ha Nguyen and Qiaoyun Xie and Constance Katelaris",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 2019 IEEE International Geoscience and Remote Sensing Symposium ; Conference date: 28-07-2019 Through 02-08-2019",

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doi = "10.1109/IGARSS.2019.8898796",

language = "English",

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Huete, A, Tran, NN, Nguyen, H, Xie, Q & Katelaris, C 2019, Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools. in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium., 8898796, IEEE, Institute of Electrical and Electronics Engineers, pp. 5429-5432, 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8898796

Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools. / Huete, Alfredo; Tran, Ngoc Nguyen; Nguyen, Ha et al.
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 5429-5432 8898796.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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AU - Katelaris, Constance

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

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