STUDYING EVOLUTION OF HYDROTHERMAL ALTERATION MATERIALS IN THE TURRIALBA VOLCANO TROUGH MULTISPECTRAL AND HYPERSPECTRAL IMAGES
- 1National Institute for Aerospace Technology, INTA. Ctra. de Ajalvir km 4 s/n, 28850 Torrejón de Ardoz, Spain
- 2Department of Engineering and Land Morphology, Technical University of Madrid, UPM. Ramiro de Maeztu, 7, 28040 Madrid, Spain
- 3Univ Lyon, INSA-Lyon, CNRS, LIRIS, UMR5205, F-69621, Villeurbanne, France
- 4Univ Lyon, INSA-Lyon, CNRS, INRIA, LIRIS, UMR5205, F-69621, Villeurbanne, France
- 5Université Savoie Mont Blanc, Polytech Annecy-Chambéry, LISTIC, B.P. 80439, Annecy-le-Vieux, F-74944 Annecy Cedex, France
- 6Universidade Federal do Rio de Janeiro, COPPE, Cidade Universitária, Rio de Janeiro, Brazil
- 7Costa Rica University, UCR, Campus UCR 4058 San José, Costa Rica
- 8Universidade Federal de Alagoas-UFAL, Campus AC Simões, Maceió, Brazil
Keywords: Hydrothermal Alteration Materials, Time Series, Anomaly Detection (AD), Copernicus, Turrialba Volcano
Abstract. The aim of this work is to develop a geospatial methodology for the analysis of the time evolution of The Turrialba volcano using different automatic imaging techniques compared to expert-based remote sensing techniques. Change detection of hydrothermal alteration materials in relation with time series from multisensor data acquired in spectral ranges of the visible (VIS) and short wave infrared (SWIR) have been calculated. We used for this purpose multispectral and hyperspectral scenes of the Sentinel 2, ALI and Hyperion sensors, respectively, on four dates from 2013 and 2018. This work adopts a multi-source approach, applied to the analysis of the correlations between hydrothermal materials and spectral anomalies in The Turrialba volcano complex, located in The Central Volcanic Range (Costa Rica).
An expert-based technique called Crosta’s technique for detecting hydrothermal materials have been applied. We have chosen four variables for generating a different Principal Component Analysis (PCA) for groups of channels, two highly reflective and two highly absorptive for each mineral. We have tested another technique to detect hydrothermal materials based on a discrete spectral profile analysis and an unsupervised data mining approach. In other sense, we have applied an automatic technique called anomaly detection to compare with the hydrothermal materials results. Results are presented as an approach based on a comparison of two different strategies whose main future interest lies in the automated identification of patterns of hydrothermally altered materials without prior knowledge or poor information about the area, which has relevant implications in image-based prospecting.