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Remote Sens. 2016, 8(7), 566; doi:10.3390/rs8070566

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

1
School of Forestry and Natural Environment, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
2
Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Research Unit for Climatology and Meteorology Applied to Agriculture (CREA-CMA), Rome 00186, Italy
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Diofantos Hadjimitsis, Luigi Boschetti, Kyriacos Themistocleous, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 28 March 2016 / Revised: 21 June 2016 / Accepted: 30 June 2016 / Published: 5 July 2016
View Full-Text   |   Download PDF [5015 KB, uploaded 5 July 2016]   |  

Abstract

Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery. View Full-Text
Keywords: burn severity; Composite Burn Index (CBI); post-fire dynamics; redundancy analysis burn severity; Composite Burn Index (CBI); post-fire dynamics; redundancy analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dragozi, E.; Gitas, I.Z.; Bajocco, S.; Stavrakoudis, D.G. Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece. Remote Sens. 2016, 8, 566.

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