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Auteur David Freudenberger |
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Mapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)
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Titre : Mapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine Type de document : Article/Communication Auteurs : Jiyu Liu, Auteur ; David Freudenberger, Auteur ; Lim Samsung, Auteur Année de publication : 2022 Article en page(s) : pp 1867 - 1897 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse spectrale
[Termes IGN] approche hiérarchique
[Termes IGN] Australie
[Termes IGN] carte thématique
[Termes IGN] écosystème forestier
[Termes IGN] Google Earth Engine
[Termes IGN] image infrarouge
[Termes IGN] image Landsat-8
[Termes IGN] incendie
[Termes IGN] Indien (océan)
[Termes IGN] segmentation d'image
[Termes IGN] utilisation du sol
[Termes IGN] zone sinistréeRésumé : (auteur) In Australia, fire has become part of the natural ecosystem. Severe fires have devastated Australia's unique forest ecosystems due to the global climate change. In this study, we integrated a multi-resolution segmentation method and a hierarchical classification framework based on expert-based knowledge to classify the burned areas and land-uses in Kangaroo Island, South Australia. Using an object-based image classification framework that combines colour and shape features from input layers, we demonstrated that the objects segmented from the multi-source data lead to a higher accuracy in classification with an overall accuracy of 90.2% and a kappa coefficient of 85.2%. On the other hand, the single source data from post-fire Landsat-8 imagery showed an overall accuracy of 87.4% which is also statistically acceptable. According to our experiment results, more than 30.44% of the study area was burned during the 2019–2020 ‘Black-Summer’ fire season in Australia. Among the burned areas, high severity accounted for 12.14%, moderate severity for 11.48%, while low severity was 6.82%. For unburned areas, farmland accounted for 45.52% of the study area, of which about one-third was affected by the disturbances other than fire. The remaining area consists of 19.42% unaffected forest, 3.48% building and bare land, and 1.14% water. The comparison analysis shows that our object-based image classification framework takes full advantage of the multi-source data and generates the edges of burned areas more clearly, which contributes to the improved fire management and control. Numéro de notice : A2022-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2098066 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.1080/19475705.2022.2098066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102171
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 1867 - 1897[article]