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

Τύπος:	Κεφάλαιο σε πρακτικά συνεδρίου
Τίτλος:	Towards a fully automatic processing chain for operationally mapping burned areas countrywide exploiting 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:	27/06/2019
Περίληψη:	Burned area mapping is essential for quantifying the environmental impact of wildfires, for compiling statistics, and for designing effective short- to mid-term impact mitigation measures. 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 the 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 presents a preliminary methodology for mapping burned areas using Sentinel-2 data, which aims to eliminate user interaction and achieve mapping accuracy that is acceptable for operational use. It follows an object-based image analysis (OBIA) approach, whereby the initial image is segmented into a set of adjacent and non-overlapping small regions (objects). The most unambiguous of them are labeled automatically through a set of empirical rules that combine information extracted from both a pre-fire Sentinel-2 image and a post-fire one. The burned area is finally delineated following a supervised learning approach, whereby a Support Vector Machine (SVM) is trained using the labeled objects and subsequently applied to the whole image considering a set of optimally selected object-level features. Preliminary results on a set of recent large wildfires in Greece indicate that the proposed methodology constitutes a solid basis for fully automating the burned area mapping process.
Γλώσσα:	Αγγλικά
Σελίδες:	9
DOI:	10.1117/12.2535816
Θεματική κατηγορία:	[EL] Γεωεπιστήμες και Επιστήμες Περιβάλλοντος[EN] Earth and related Environmental Sciences
Λέξεις-κλειδιά:	operational burned area mapping; object-based image analysis (OBIA); quick shift segmentation; Sentinel-2; automatic training patterns classification; machine learning
Κάτοχος πνευματικών δικαιωμάτων:	© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
Όροι και προϋποθέσεις δικαιωμάτων:	One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11174/2535816/Towards-a-fully-automatic-processing-chain-for-operationally-mapping-burned/10.1117/12.2535816.short
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11174.toc
Τίτλος πηγής δημοσίευσης:	Proceedings SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019)
Τόμος:	11174
Σελίδες τεκμηρίου (στην πηγή):	Article no 1117405
Σειρά δημοσίευσης:	Proceedings of SPIE
Όνομα εκδήλωσης:	Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019)
Τοποθεσία εκδήλωσης:	Paphos, Cyprus
Ημ/νία έναρξης εκδήλωσης:	18/03/2019
Ημ/νία λήξης εκδήλωσης:	21/03/2019
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”.
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