Vol. 15, No. 1, 1-8, 2016

Monitoring post-fire forest recovery using multi-temporal Digital Surface Models generated from different platforms
Irene Aicardi, Matteo Garbarino, Andrea Lingua, Emanuele Lingua, Raffaella Marzano, and Marco Piras

Wildfires can greatly affect forest dynamics. Given the alteration of fire regimes foreseen globally due to climate and land use changes, greater attention should be devoted to prevention and restoration activities. Concerning in particular post-fire restoration actions, it is fundamental, together with a better understanding of ecological processes resulting from the disturbance, to define techniques and protocols for long-term monitoring of burned areas. This paper presents the results of a study conducted within an area affected by a stand-replacing crown fire (Verrayes, Aosta (AO), Italy) in 2005, which is part of a long-term monitoring research on post-fire restoration dynamics. We performed a change detection analysis through a time sequence (2008-2015) of DSMs (Digital Surface Models) obtained from LiDAR (ALS - Airborne Laser Scanner) and digital images (UAV - Unmanned Aerial Vehicle flight) to test the ability of the systems (platform + sensor) to identify the ongoing processes. New technologies providing high-resolution information and new devices (i.e. UAV) able to acquire geographic data "on demand" demonstrated great potential for monitoring post disturbance recovery dynamics of vegetation.

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DOI: 10.12760/01-2016-1-01

Submitted: 17 Nov 2015
Revised: 10 Feb 2016
Accepted: 12 Feb 2016
Published: 12 Apr 2016
Responsible editor: Ioannis Gitas/Rainer Reuter

Aicardi I, M Garbarino, A Lingua, E Lingua, R Marzano & M Piras, 2016. Monitoring post-fire forest recovery using multi-temporal Digital Surface Models generated from different platforms. EARSeL eProceedings, 15(1): 1-8

EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France


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ISSN 1729-3782