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Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective

Fassnacht, Fabian Ewald ORCID iD icon 1; Schmidt-Riese, Ephraim 1; Kattenborn, Teja 1; Hernández, Jaime
1 Institut für Geographie und Geoökologie (IFGG), Karlsruher Institut für Technologie (KIT)

Abstract:
Characterizing the spatial variability of the severity of wildfires is important to assess ecological and economic consequences and to coordinate mitigation strategies. Vegetation indices such as the differenced Normalized Burn Ratio (dNBR) have become a standard tool to assess burn or fire severity across larger areas and are being used operationally. Despite the frequent application of dNBR-like vegetation indices, it is not yet fully understood which variables exactly drive the variability in dNBR observed by multispectral satellites. One reason for this is the lack of high quality prefire information about vegetation structure and composition. Consequently, the influence of prefire vegetation composition and other potentially influential variables such as cast shadows has hardly been examined. Here, we use very high resolution Unmanned Aerial System (UAS) orthoimages collected briefly before and after the large wildfires in Central Chile in the fire season 2016/2017 to derive variables related to the pre- and postfire landscape composition and structure. The variables are used as predictors in Generalized Additive Models (GAM) to explain the spatial variability in dNBR and RdNBR pixel values as observed by Sentinel-2. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000126831
Veröffentlicht am 27.11.2020
Originalveröffentlichung
DOI: 10.1016/j.jag.2020.102262
Dimensions
Zitationen: 9
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 03.2021
Sprache Englisch
Identifikator ISSN: 0303-2434
KITopen-ID: 1000126831
Erschienen in International journal of applied earth observation and geoinformation
Verlag Elsevier B.V.
Band 95
Seiten Article no: 102262
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 11.11.2020
Schlagwörter UAS, dNBR variability, Wildfire, Sentinel-2, RdNBR, Shadows
Nachgewiesen in Dimensions
Web of Science
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