1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Άρθρο σε περιοδικό
Τίτλος:	Automated burned scar mapping using Sentinel-2 imagery
Συγγραφέας:	[EL] Σταυρακούδης, Δημήτριος[EN] Stavrakoudis, Dimitris G. [EL] Καταγής, Θωμάς[EN] Katagis, Thomas [EL] Μηνάκου, Χαραλαμπία[EN] Minakou, Chara [EL] Γήτας, Ιωάννης[EN] Gitas, Ioannis Z. I.Z.
Ημερομηνία:	30/06/2020
Περίληψη:	The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.
Γλώσσα:	Αγγλικά
Σελίδες:	20
DOI:	10.4236/jgis.2020.123014
ISSN:	2151-1950
Θεματική κατηγορία:	[EL] Γεωεπιστήμες και Επιστήμες Περιβάλλοντος[EN] Earth and related Environmental Sciences
Λέξεις-κλειδιά:	Operational Burned Area Mapping; Multiple Spectral-Spatial Classification (MSSC); Sentinel-2; Automatic Training Patterns Classification; machine learning
Κάτοχος πνευματικών δικαιωμάτων:	Copyright © 2020 by authors and Scientific Research Publishing Inc.
Όροι και προϋποθέσεις δικαιωμάτων:	This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.
Διατίθεται ανοιχτά στην τοποθεσία:	https://doi.org/10.4236/jgis.2020.123014
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.scirp.org/journal/paperinformation.aspx?paperid=101279
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.scirp.org/journal/jgis/
Τίτλος πηγής δημοσίευσης:	Journal of Geographic Information System
Τεύχος:	3
Τόμος:	12
Σελίδες τεκμηρίου (στην πηγή):	221-240
Σημειώσεις:	This research is funded in the context of the project “Development of advanced algorithm and open-source software for automated burned area mapping using high-resolution data” (MIS 5005537) under the call for proposals “Supporting researchers with emphasis on new researchers” (EDULLL 34). The project is co-financed by Greece and the European Union (European Social Fund—ESF) by the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014-2020”.
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες