Please use this identifier to cite or link to this item: http://localhost:80/jspui/handle/123456789/901
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dc.contributor.authorKagombe, Joram K.-
dc.contributor.authorKiama, Stephen M.-
dc.contributor.authorKungu, James B.-
dc.date.accessioned2020-05-28T12:19:22Z-
dc.date.available2020-05-28T12:19:22Z-
dc.date.issued2020-04-30-
dc.identifier.citationISSN 2224-3186 (Paper) ISSN (Online) Vol.10, No.8, 2020en_US
dc.identifier.issn2224-3186(print) 2225-0921(online)-
dc.identifier.otherDOI: 10.7176/JNSR/10-8-03-
dc.identifier.urihttp://localhost:8282/jspui/handle/123456789/901-
dc.descriptionArticle was first published on www.iiste.orgen_US
dc.description.abstractWe successfully used optical remote sensing approach to test the skills of post-classification change detection technique as well as techniques of circumventing the challenges of cloud/cloud-shadow contamination and of working in a data-scarce environment in tropical humid highlands. The aim was to generate an accurate estimate of current land cover distribution map and analyze land-cover change around Ndakaini area in Kenya. Landsat imageries (TM and ETM+) acquired between 1985 and 2011 and corresponding to the study area was selected. Employing bands 3 and 4 of respective Landsat images, thresholding techniques, Boolean and masking operations were implemented in detecting cloud/cloud-shadows and subsequent removal and filling of gaps. In absence of other historical ancillary data about land cover types, a total of 278 points across the study area were captured from Google Earth and used to evaluate the accuracy of each of the generated land cover maps. From the results, cloud/cloud-shadow gaps were reduced immensely (e.g. 90% for the 1985 image and 82% for the 2011 image). With regard to quality of classification outputs, the respective land cover/land-use maps of 2000, 2005 and 2010 anniversaries had fairly high level of overall accuracy (64%, 79% and 68% respectively) and Kappa statistic (0.47, 0.69 and 0.53 respectively) while classification outputs of 1985 and 1995 yielded slightly lower overall accuracy (60%) and Kappa statistic (0.42). Post-classification change involving three land cover classes, tea plantation, forest/woodlot and annual crop fields denoted as others were successfully determined and conclusions based on trend analysis drawn. The satisfactory results of this study imply the usefulness of post-classification change detection method in generating information about land cover dynamics in tropical humid highlands especially when coupled with robust techniques that adequately circumvent the cloud and cloud-shadow problem and scarcity of ancillary data often common in these areas. Keywords: Post-classification change detection, thresholding and Boolean techniques, landcover change, tropical humid-highlandsen_US
dc.language.isoenen_US
dc.publisherJournal of Natural Sciences Researchen_US
dc.relation.ispartofseriesJournal of Natural Sciences Research;8-
dc.subjectPost-classification change detectionen_US
dc.subjecttropical humid-highlandsen_US
dc.subjectthresholding and Boolean techniquesen_US
dc.subjectlandcover changeen_US
dc.titleLand cover mapping and change analysis in tropical humid-highlands: Case of Ndakaini water reservoir in Central Kenyaen_US
dc.typeArticleen_US
dc.subject.ThematicAreafbemen_US
dc.description.RegionalProgrammehqen_US
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