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

Show simple item record Ali, Iftikhar Cawkwell, Fiona Dwyer, Edward Green, Stuart 2018-07-17T10:36:17Z 2018-07-17T10:36:17Z 2017
dc.identifier.citation Ali, 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.2016.2561618 en
dc.identifier.volume 10 en
dc.identifier.issued 7 en
dc.identifier.startpage 3254 en
dc.identifier.endpage 3264 en
dc.identifier.issn 1939-1404
dc.identifier.doi 10.1109/JSTARS.2016.2561618
dc.description.abstract More 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.sponsorship Teagasc (Walsh Fellowship) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher IEEE en
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.subject Fuzzy reasoning en
dc.subject Geophysical techniques en
dc.subject Land cover en
dc.subject Land use en
dc.subject Learning (artificial intelligence) en
dc.subject Neural nets en
dc.subject Regression analysis en
dc.subject Remote sensing by radar en
dc.subject Vegetation en
dc.subject Ad 2001 to 2012 en
dc.subject Alos-2 en
dc.subject Anfis model en
dc.subject Grange en
dc.subject Ireland en
dc.subject Modis product en
dc.subject Moorepark en
dc.subject Radarsat2 en
dc.subject Sentinel en
dc.subject Tandem-x en
dc.subject Terrasar-x en
dc.subject Adaptive neuro-fuzzy inference system en
dc.subject Agricultural land en
dc.subject Artificial neural network en
dc.subject Dairy farming en
dc.subject Grassland biomass estimation en
dc.subject In situ measurement en
dc.subject Livestock industry en
dc.subject Machine learning approach en
dc.subject Multiple linear regression en
dc.subject Multitemporal remote sensing data en
dc.subject Pasture en
dc.subject Satellite remote sensing data en
dc.subject Spaceborne sensors en
dc.subject Vegetation index en
dc.subject Winter fodder en
dc.subject Agriculture en
dc.subject Biological system modeling en
dc.subject Biomass en
dc.subject Estimation en
dc.subject Monitoring en
dc.subject Remote sensing en
dc.subject Satellites en
dc.subject Biomass estimation en
dc.subject Machine learning en
dc.subject Managed grassland en
dc.subject Time series en
dc.title Modeling managed grassland biomass estimation by using multitemporal remote sensing data - a machine learning approach en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Fiona Cawkwell, Geography, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en 2018-07-16T11:37:45Z
dc.description.version Accepted Version en
dc.internal.rssid 445714653
dc.contributor.funder Teagasc en
dc.description.status Peer reviewed en
dc.identifier.journaltitle IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress en

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