Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series
Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15–20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stati...
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description | Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15–20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.
8-year average MTCI-based map of onset of flowering of grassland as a |
doi_str_mv | 10.1016/j.scitotenv.2016.11.004 |
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8-year average MTCI-based map of onset of flowering of grassland as a source of grass pollen. The map depicts the spatial variation in the onset of flowering coincidental with the start of pollen season. [Display omitted]
•A novel method was developed to estimate onset of pollen season from RS data.•Strong positive correlation between the onset of pollen season and pollen data•Onset of pollen season for birch and grass is mapped at a high spatial resolution.</description><identifier>ISSN: 0048-9697</identifier><identifier>EISSN: 1879-1026</identifier><identifier>DOI: 10.1016/j.scitotenv.2016.11.004</identifier><identifier>PMID: 27856057</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Aerobiology ; Allergens - analysis ; Betula - physiology ; Birch pollen ; Europe ; Grass pollen ; Hay fever ; MERIS MTCI ; Onset of birch flowering ; Onset of grass flowering ; Onset of greenness ; Phenology ; Poaceae - physiology ; Pollen ; Predicting model ; Satellite Imagery ; Seasons ; Spatio-Temporal Analysis ; United Kingdom</subject><ispartof>The Science of the total environment, 2017-02, Vol.578, p.586-600</ispartof><rights>2016 Elsevier B.V.</rights><rights>Copyright © 2016 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-2f9bfabb24389ddc0f270e593a099d3b62c3eb6b245975c80ce7b9178ddc2f0e3</citedby><cites>FETCH-LOGICAL-c453t-2f9bfabb24389ddc0f270e593a099d3b62c3eb6b245975c80ce7b9178ddc2f0e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.scitotenv.2016.11.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27856057$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Khwarahm, Nabaz R.</creatorcontrib><creatorcontrib>Dash, Jadunandan</creatorcontrib><creatorcontrib>Skjøth, C.A.</creatorcontrib><creatorcontrib>Newnham, R.M.</creatorcontrib><creatorcontrib>Adams-Groom, B.</creatorcontrib><creatorcontrib>Head, K.</creatorcontrib><creatorcontrib>Caulton, Eric</creatorcontrib><creatorcontrib>Atkinson, Peter M.</creatorcontrib><title>Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series</title><title>The Science of the total environment</title><addtitle>Sci Total Environ</addtitle><description>Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15–20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.
8-year average MTCI-based map of onset of flowering of grassland as a source of grass pollen. The map depicts the spatial variation in the onset of flowering coincidental with the start of pollen season. [Display omitted]
•A novel method was developed to estimate onset of pollen season from RS data.•Strong positive correlation between the onset of pollen season and pollen data•Onset of pollen season for birch and grass is mapped at a high spatial resolution.</description><subject>Aerobiology</subject><subject>Allergens - analysis</subject><subject>Betula - physiology</subject><subject>Birch pollen</subject><subject>Europe</subject><subject>Grass pollen</subject><subject>Hay fever</subject><subject>MERIS MTCI</subject><subject>Onset of birch flowering</subject><subject>Onset of grass flowering</subject><subject>Onset of greenness</subject><subject>Phenology</subject><subject>Poaceae - physiology</subject><subject>Pollen</subject><subject>Predicting model</subject><subject>Satellite Imagery</subject><subject>Seasons</subject><subject>Spatio-Temporal Analysis</subject><subject>United Kingdom</subject><issn>0048-9697</issn><issn>1879-1026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1u2zAQhIkiReOkfYWGx1ykkpQoikfDaH5QB7k06JGgqFVCQ6YULm2gbx-6Tn1t97LA8ptdYoaQK85KznjzbVOi82lKEPalyIOS85Kx-gNZ8FbpgjPRnJFFnrSFbrQ6JxeIG5ZLtfwTOReqlQ2TakF-Pdh59uGZphegnY_uhdrQ0-doEek8jSMEimBxCkh9-EM9_aA7PEjQJhhHnyATAadIk99CgRA94GfycbAjwpf3fkmebr7_XN0V68fb-9VyXbhaVqkQg-4G23Wirlrd944NQjGQurJM677qGuEq6Jr8LrWSrmUOVKe5ajMrBgbVJbk-7p3j9LoDTGbr0eVv2QDTDg1vpa61zIr_QGuudCOkzKg6oi5OiBEGM0e_tfG34cwcAjAbcwrAHAIwnJtsd1Z-fT-y67bQn3R_Hc_A8ghAdmXvIR4WQXDQ-wgumX7y_zzyBtOjnBA</recordid><startdate>20170201</startdate><enddate>20170201</enddate><creator>Khwarahm, Nabaz R.</creator><creator>Dash, Jadunandan</creator><creator>Skjøth, C.A.</creator><creator>Newnham, R.M.</creator><creator>Adams-Groom, B.</creator><creator>Head, K.</creator><creator>Caulton, Eric</creator><creator>Atkinson, Peter M.</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7ST</scope><scope>7U7</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>20170201</creationdate><title>Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series</title><author>Khwarahm, Nabaz R. ; Dash, Jadunandan ; Skjøth, C.A. ; Newnham, R.M. ; Adams-Groom, B. ; Head, K. ; Caulton, Eric ; Atkinson, Peter M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-2f9bfabb24389ddc0f270e593a099d3b62c3eb6b245975c80ce7b9178ddc2f0e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aerobiology</topic><topic>Allergens - analysis</topic><topic>Betula - physiology</topic><topic>Birch pollen</topic><topic>Europe</topic><topic>Grass pollen</topic><topic>Hay fever</topic><topic>MERIS MTCI</topic><topic>Onset of birch flowering</topic><topic>Onset of grass flowering</topic><topic>Onset of greenness</topic><topic>Phenology</topic><topic>Poaceae - physiology</topic><topic>Pollen</topic><topic>Predicting model</topic><topic>Satellite Imagery</topic><topic>Seasons</topic><topic>Spatio-Temporal Analysis</topic><topic>United Kingdom</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khwarahm, Nabaz R.</creatorcontrib><creatorcontrib>Dash, Jadunandan</creatorcontrib><creatorcontrib>Skjøth, C.A.</creatorcontrib><creatorcontrib>Newnham, R.M.</creatorcontrib><creatorcontrib>Adams-Groom, B.</creatorcontrib><creatorcontrib>Head, K.</creatorcontrib><creatorcontrib>Caulton, Eric</creatorcontrib><creatorcontrib>Atkinson, Peter M.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Environment Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>The Science of the total environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khwarahm, Nabaz R.</au><au>Dash, Jadunandan</au><au>Skjøth, C.A.</au><au>Newnham, R.M.</au><au>Adams-Groom, B.</au><au>Head, K.</au><au>Caulton, Eric</au><au>Atkinson, Peter M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series</atitle><jtitle>The Science of the total environment</jtitle><addtitle>Sci Total Environ</addtitle><date>2017-02-01</date><risdate>2017</risdate><volume>578</volume><spage>586</spage><epage>600</epage><pages>586-600</pages><issn>0048-9697</issn><eissn>1879-1026</eissn><abstract>Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15–20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.
8-year average MTCI-based map of onset of flowering of grassland as a source of grass pollen. The map depicts the spatial variation in the onset of flowering coincidental with the start of pollen season. [Display omitted]
•A novel method was developed to estimate onset of pollen season from RS data.•Strong positive correlation between the onset of pollen season and pollen data•Onset of pollen season for birch and grass is mapped at a high spatial resolution.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>27856057</pmid><doi>10.1016/j.scitotenv.2016.11.004</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Aerobiology Allergens - analysis Betula - physiology Birch pollen Europe Grass pollen Hay fever MERIS MTCI Onset of birch flowering Onset of grass flowering Onset of greenness Phenology Poaceae - physiology Pollen Predicting model Satellite Imagery Seasons Spatio-Temporal Analysis United Kingdom |
title | Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series |
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