A Spatial-Temporal Modeling Approach to Reconstructing Land-Cover Change Trajectories from Multi-temporal Satellite Imagery.
Temporal trajectories of land-cover change provide important information on landscape dynamics that are critical to our understanding of complex human–environment adaptive systems. The increasing availability of long time series of satellite images, especially the recent free release of multi-decada...
| Publicado en: | Annals of the Association of American Geographers Vol. 102; no. 6; pp. 1329 - 1348 |
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| Autores principales: | , |
| Formato: | Case Study |
| Publicado: |
Taylor & Francis Ltd
Nov2012
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=82153342&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 82153342 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00045608 AAG jtl: Annals of the Association of American Geographers issn: 00045608 maglogo: Y pubinfo: dt: Nov2012 vid: 102 iid: 6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 82153342 10.1080/00045608.2011.596357 ppf: 1329 ppct: 19 formats: tig: atl: A Spatial-Temporal Modeling Approach to Reconstructing Land-Cover Change Trajectories from Multi-temporal Satellite Imagery. aug: au: Liu, Desheng Cai, Shanshan affil: Department of Geography and Department of Statistics, The Ohio State University Department of Geography, The Ohio State University su: Ohio Land cover Remote-sensing images Landsat satellites Markov random fields sug: subj: Ohio Land cover Remote-sensing images Landsat satellites Markov random fields keyword: change detection land-cover change trajectories Landsat imagery post-classification comparison spatial-temporal contextual information. comparación post-clasificación detección de cambios imágenes Landsat información contextual espacio-temporal trayectorias de cambio de cobertura terrestre change detection land-cover change trajectories Landsat imagery post-classification comparison spatial-temporal contextual information. comparación post-clasificación detección de cambios imágenes Landsat información contextual espacio-temporal trayectorias de cambio de cobertura terrestre ab: Temporal trajectories of land-cover change provide important information on landscape dynamics that are critical to our understanding of complex human–environment adaptive systems. The increasing availability of long time series of satellite images, especially the recent free release of multi-decadal Landsat satellite archive, presents a great opportunity to improve our ability to detect land-cover change over multiple dates and advance land change science. In this article, a spatial-temporal modeling approach is developed for reconstructing land-cover change trajectories from time series of satellite images. The change detection method represents an enhancement to the conventional post-classification comparison. The key innovation lies in the use of Markov random field theory to model spatial-temporal contextual information explicitly in the classification of time series images. When evaluated using a time series of seven Landsat images in a case study of southeast Ohio, the spatial-temporal modeling approach yielded significantly more accurate and consistent trajectories of land-cover change than conventional non-contextual approaches. The results from the case study demonstrate the effectiveness of the change detection method in reconstructing land-cover change trajectories and also highlight the utility of spatial-temporal contextual information in improving the accuracy and consistency of land-cover classifications across space and time. pubtype: Academic Journal doctype: Case Study src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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