
Aplicación de imágenes Sentinel-1 y Sentinel-2 en la detección y delineación de información de crisis de desastres naturales en el marco de los servicios Copernicus EMS
Author(s) -
U. Donezar-Hoyos,
A. Larrañaga Urien,
A. Tamés-Noriega,
C. Sánchez-Gil,
L. Albizua-Huarte,
R. Ciriza-Labiano,
F. del Barrio-Arellano
Publication year - 2017
Publication title -
revista de teledetección
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.271
H-Index - 9
eISSN - 1988-8740
pISSN - 1133-0953
DOI - 10.4995/raet.2017.8896
Subject(s) - radar , remote sensing , vegetation (pathology) , cartography , geology , geography , computer science , telecommunications , medicine , pathology
This study shows the inclusion of Sentinel-1 and Sentinel-2 images in the workflows to obtain of crisis information of different types of events and their applicability in the detection and monitoring of those events. Sentinel is an Earth Observation (EO) program that is currently being developed by the European Space Agency (ESA) in the scope of the Copernicus program operative since April 2012, formerly known as Global Monitoring for Environment and Security (GMES). This program comprises six missions, out of which three are active, Sentinel-1 that provides radar images, Sentinel-.2 providing High Resolution optical images and Sentinel-3 developed to support GMES ocean, land, atmospheric, emergency, security and cryospheric applications. The present paper describes the use of Sentinel-1 radar to detect and delineate flooded areas, and the MultiTemporal Coherence (MTC) analysis applied with pre and post-event images to delimit and monitor burnt areas and lava flows. With respect to Sentinel-2, its high spectral resolution bands allowed the delineation of burnt areas by calculating differences of vegetation and burnt indices using pre and postevent images. Results using Sentinel-1 and Sentinel-2 data were compared with results using higher spatial resolution images, both optical and radar. In all cases, the usability of Sentinel images was proven