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An Automatic Process Chain for Detecting Burn Scars Using Sentinel-2 Data

Bettinger, Michaela and Martinis, Sandro and Plank, Simon Manuel (2017) An Automatic Process Chain for Detecting Burn Scars Using Sentinel-2 Data. In: Conference Proceedings, p. 8. EARSeL 11th 2017, 25.-27. Sep. 2017, Chania, Greece.

Full text not available from this repository.

Abstract

The advantage of the new Sentinel-2 mission is its high geometric resolution (10 m) with a high temporal resolution (~ 5 days) at the same time. To make use of this for the monitoring of burn scars, this paper presents a new method for the automatic detection of burn scars on the basis of Sentinel-2 data. In order to develop a globally applicable method, 108 mono- and bi-temporal features of seven global distributed test sites are analysed in a discriminant analysis to examine their potential for the identification of burned areas. Based on these results, a two-phase algorithm is proposed. The first phase serves to identify seed pixels which have a high probability to be related to burned areas. Due to its simplicity, a threshold-based approach is used for this purpose. In the second phase, a refinement of the burned areas is accomplished by examining the neighbourhood of the seed pixels through a region growing method based on Support Vector Machines. The validation of the proposed approach shows encouraging high user accuracies of 95 - 99%. The producers accuracies vary between 80 - 90%.

Item URL in elib:https://elib.dlr.de/116007/
Document Type:Conference or Workshop Item (Speech)
Title:An Automatic Process Chain for Detecting Burn Scars Using Sentinel-2 Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bettinger, MichaelaMichaela.Bettinger (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Plank, Simon Manuelsimon.plank (at) dlr.dehttps://orcid.org/0000-0002-5793-052X
Date:25 September 2017
Journal or Publication Title:Conference Proceedings
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:p. 8
Status:Published
Keywords:burn scar detection, Sentinel-2, automatic approach, rapid mapping, Support Vector Machine
Event Title:EARSeL 11th 2017
Event Location:Chania, Greece
Event Type:international Conference
Event Dates:25.-27. Sep. 2017
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Bettinger, Michaela
Deposited On:04 Dec 2017 12:00
Last Modified:15 Mar 2018 14:13

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