Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/45182
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dc.contributor.authorHamad, Rahel-
dc.contributor.authorBalzter, Heiko-
dc.contributor.authorKolo, Kamal-
dc.date.accessioned2019-08-12T14:02:10Z-
dc.date.available2019-08-12T14:02:10Z-
dc.date.issued2018-09-25-
dc.identifier.citationSustainability, 2018, 10(10), 3421.en
dc.identifier.issn2071-1050-
dc.identifier.urihttps://www.mdpi.com/2071-1050/10/10/3421en
dc.identifier.urihttp://hdl.handle.net/2381/45182-
dc.description.abstractMulti-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.en
dc.description.sponsorshipThis work forms a part of a study supported by the Scientific Research Centre (SRC), Soran University and the Centre for Landscape and Climate Research (CLCR), University of Leicester. H.B. was supported by the Royal Society Wolfson Research Merit Award, 2011/R3 and the NERC National Centre for Earth Observation in the UK.en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000448559400063&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8c4e325952a993be76947405d4bce7d5-
dc.rightsCopyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectScience & Technologyen
dc.subjectLife Sciences & Biomedicineen
dc.subjectGREEN & SUSTAINABLE SCIENCE & TECHNOLOGYen
dc.subjectEnvironmental Sciencesen
dc.subjectEnvironmental Studiesen
dc.subjectScience & Technology - Other Topicsen
dc.subjectEnvironmental Sciences & Ecologyen
dc.subjectland change modelleren
dc.subjectbusiness-as-usual scenarioen
dc.subjectHalgurd-Sakran National Parken
dc.subjectCA-Markov moduleen
dc.subjectmodelling LULC changeen
dc.subjectGEO-INFORMATIONen
dc.subjectCHAIN MODELSen
dc.subjectSIMULATIONen
dc.subjectCHINAen
dc.subjectVALIDATIONen
dc.titlePredicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenariosen
dc.typeJournal Articleen
dc.identifier.doi10.3390/su10103421-
dc.description.statusPeer-revieweden
dc.description.versionPublisher Versionen
dc.type.subtypeArticle;Journal-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERINGen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environmenten
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/GIS and Remote Sensingen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/Physical Geographyen
dc.dateaccepted2018-09-15-
Appears in Collections:Published Articles, Dept. of Geography

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