Modeling managed grassland biomass estimation by using multitemporal remote sensing data - a machine learning approach

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dc.contributor.authorAli, Iftikhar
dc.contributor.authorCawkwell, Fiona
dc.contributor.authorDwyer, Edward
dc.contributor.authorGreen, Stuart
dc.contributor.funderTeagascen
dc.date.accessioned2018-07-17T10:36:17Z
dc.date.available2018-07-17T10:36:17Z
dc.date.issued2017
dc.date.updated2018-07-16T11:37:45Z
dc.description.abstractMore than 80% of agricultural land in Ireland is grassland, which is a major feed source for the pasture based dairy farming and livestock industry. Many studies have been undertaken globally to estimate grassland biomass by using satellite remote sensing data, but rarely in systems like Ireland's intensively managed, but small-scale pastures, where grass is grazed as well as harvested for winter fodder. Multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to estimate the grassland biomass (kg dry matter/ha/day) of two intensively managed grassland farms in Ireland. For the first test site (Moorepark) 12 years (2001-2012) and for second test site (Grange) 6 years (2001- 2005, 2007) of in situ measurements (weekly measured biomass) were used for model development. Five vegetation indices plus two raw spectral bands (RED=red band, NIR=Near Infrared band) derived from an 8-day MODIS product (MOD09Q1) were used as an input for all three models. Model evaluation shows that the ANFIS (RM2moorepark = 0.85, RMSEMoorepark = 11.07; RGrange2 = 0.76, RMSEGrange = 15.35) has produced improved estimation of biomass as compared to the ANN and MLR. The proposed methodology will help to better explore the future inflow of remote sensing data from spaceborne sensors for the retrieval of different biophysical parameters, and with the launch of new members of satellite families (ALOS-2, Radarsat2, Sentinel, TerraSAR-X, TanDEM-X/L) the development of tools to process large volumes of image data will become increasingly important.en
dc.description.sponsorshipTeagasc (Walsh Fellowship)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAli, I., Cawkwell, F., Dwyer, E. and Green, S. (2017) 'Modeling managed grassland biomass estimation by using multitemporal remote sensing data - a machine learning approach', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), pp. 3254-3264. DOI: 10.1109/JSTARS.2016.2561618en
dc.identifier.doi10.1109/JSTARS.2016.2561618
dc.identifier.endpage3264en
dc.identifier.issn1939-1404
dc.identifier.issued7en
dc.identifier.journaltitleIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensingen
dc.identifier.startpage3254en
dc.identifier.urihttps://hdl.handle.net/10468/6463
dc.identifier.volume10en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttps://ieeexplore.ieee.org/document/7482764/
dc.rights© 2017 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
dc.subjectFuzzy reasoningen
dc.subjectGeophysical techniquesen
dc.subjectLand coveren
dc.subjectLand useen
dc.subjectLearning (artificial intelligence)en
dc.subjectNeural netsen
dc.subjectRegression analysisen
dc.subjectRemote sensing by radaren
dc.subjectVegetationen
dc.subjectAd 2001 to 2012en
dc.subjectAlos-2en
dc.subjectAnfis modelen
dc.subjectGrangeen
dc.subjectIrelanden
dc.subjectModis producten
dc.subjectMooreparken
dc.subjectRadarsat2en
dc.subjectSentinelen
dc.subjectTandem-xen
dc.subjectTerrasar-xen
dc.subjectAdaptive neuro-fuzzy inference systemen
dc.subjectAgricultural landen
dc.subjectArtificial neural networken
dc.subjectDairy farmingen
dc.subjectGrassland biomass estimationen
dc.subjectIn situ measurementen
dc.subjectLivestock industryen
dc.subjectMachine learning approachen
dc.subjectMultiple linear regressionen
dc.subjectMultitemporal remote sensing dataen
dc.subjectPastureen
dc.subjectSatellite remote sensing dataen
dc.subjectSpaceborne sensorsen
dc.subjectVegetation indexen
dc.subjectWinter fodderen
dc.subjectAgricultureen
dc.subjectBiological system modelingen
dc.subjectBiomassen
dc.subjectEstimationen
dc.subjectMonitoringen
dc.subjectRemote sensingen
dc.subjectSatellitesen
dc.subjectBiomass estimationen
dc.subjectMachine learningen
dc.subjectManaged grasslanden
dc.subjectTime seriesen
dc.titleModeling managed grassland biomass estimation by using multitemporal remote sensing data - a machine learning approachen
dc.typeArticle (peer-reviewed)en
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