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https://hdl.handle.net/1959.11/12140
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sinha, Priyakant | en |
dc.contributor.author | Kumar, Lalit | en |
dc.date.accessioned | 2013-02-25T16:14:00Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | International Journal of Remote Sensing, 34(6), p. 2162-2186 | en |
dc.identifier.issn | 1366-5901 | en |
dc.identifier.issn | 0143-1161 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/12140 | - |
dc.description.abstract | Numerous land-cover change detection techniques have been developed with varying opinions about their appropriateness and success. Decisions on the selection of the most suitable change detection method is often influenced by the study region landscape complexity and the type of data used for analysis. For different climatic areas, the method that suits best the seasonal land-cover change identification remains uncertain. In this study, 11 different binary change detection methods were tested and compared with respect to their capability in detecting land-cover change/no-change information in different seasons. The methods include image differencing (I_Diff), Improved image differencing (Imp_Diff), principal component image differencing (PC_Diff), vegetation index differencing (VI_Diff), change vector analysis (CVA), image ratioing (IR), improved image ratioing (Imp_IR), vegetation index image ratioing (VI_R), multi-date principal component analysis (PCA) using all bands (M_PCA), two-date bands PCA (B_PCA), and two-date vegetation index images PCA (VI_PCA). Multi Date Thematic Mapper (TM) data were used for a wide set of change image generation. A relatively new approach was applied for optimal threshold value determination for the separation of change/no-change areas. Research results indicated that any methods involving TM Band 4 performed better than those using TM Band 3 or 5 on each of the change periods. However, irrespective of the method used, the accuracy assessment and change/no-change validation results from normalized difference vegetation index (NDVI)-based techniques outperformed all other tested techniques in the change detection process (overall accuracy >90% and kappa value >0.85 for all six change periods). The image differencing technique was found to be marginally better than PCA and IR in most cases and any of these techniques can be used for change detection. However, because of the simplistic nature and relative ease in identifying both negative and positive changes from difference images, the NDVI differencing technique is recommended for seasonal land-cover change identification in the study region. | en |
dc.language | en | en |
dc.publisher | Taylor & Francis | en |
dc.relation.ispartof | International Journal of Remote Sensing | en |
dc.title | Binary images in seasonal land-cover change identification: a comparative study in parts of New South Wales, Australia | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1080/01431161.2012.742214 | en |
dc.subject.keywords | Photogrammetry and Remote Sensing | en |
local.contributor.firstname | Priyakant | en |
local.contributor.firstname | Lalit | en |
local.subject.for2008 | 090905 Photogrammetry and Remote Sensing | en |
local.subject.seo2008 | 960509 Ecosystem Assessment and Management of Mountain and High Country Environments | en |
local.profile.school | Environmental and Rural Science | en |
local.profile.school | School of Environmental and Rural Science | en |
local.profile.email | psinha@une.edu.au | en |
local.profile.email | lkumar@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.identifier.epublicationsrecord | une-20121128-114214 | en |
local.publisher.place | United Kingdom | en |
local.format.startpage | 2162 | en |
local.format.endpage | 2186 | en |
local.identifier.scopusid | 84870533412 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 34 | en |
local.identifier.issue | 6 | en |
local.title.subtitle | a comparative study in parts of New South Wales, Australia | en |
local.contributor.lastname | Sinha | en |
local.contributor.lastname | Kumar | en |
dc.identifier.staff | une-id:psinha | en |
dc.identifier.staff | une-id:lkumar | en |
local.profile.orcid | 0000-0002-9205-756X | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:12346 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Binary images in seasonal land-cover change identification | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Sinha, Priyakant | en |
local.search.author | Kumar, Lalit | en |
local.uneassociation | Unknown | en |
local.identifier.wosid | 000311557300017 | en |
local.year.published | 2013 | en |
local.subject.for2020 | 401304 Photogrammetry and remote sensing | en |
local.subject.seo2020 | 180201 Assessment and management of coastal and estuarine ecosystems | en |
Appears in Collections: | Journal Article School of Environmental and Rural Science |
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