Hyperspectral image analysis – examples from the Mer Bleue Arctic Surrogate Simulation Site

Prof. Margaret Kalacska (1) & Dr. Pablo Arroyo (2)

McGill University(1), NRC(2)

Dr. Margaret Kalacska is an Associate Professor in Geography at McGill University. She holds a PhD degree from the University of Alberta in Earth and Atmospheric Sciences with a specialization in remote sensing. Her research focuses on the application of remote sensing to environmental questions. She is particularly interested in historical land use change as well as hyperspectral data analysis at all scales from field spectroscopy to satellite imagery. She co-led the first Canadian airborne hyperspectral mission to Costa Rica (MAC13) for tropical forest carbon assessments. In her most research Dr. Kalacska is incorporating hyperspectral data at the field, UAV and airborne levels for biodiversity conservation and to quantify vegetation characteristics in aquatic, peatland and forest environments. She teaches remote sensing and GIS at McGill University.

Dr. Pablo Arroyo is a Research Officer at the National Research Council Canada focused on environmental applications of hyperspectral remote sensing. His past research has been highly multidisciplinary including sustainable forestry, landscape ecology; socio-economic applications of GIS and remote sensing to mention a few. He co-led the first Canadian airborne hyperspectral mission to Costa Rica (MAC13) for tropical forest carbon assessments. Dr. Arroyo’s current research encompasses a multiscale approach (bottom-up) to remote sensing aiming to integrate ground level spectroscopy to airborne hyperspectral and satellite imagery. In addition, his work explores the use of UAV hyperspectral as a mean to explain local to landscape patterns (e.g. vegetation, biodiversity, biochemical processes).

Abstract

(English) Hyperspectral image analysis – examples from the Mer Bleue Arctic Surrogate Simulation Site (Prof. Margaret Kalacska, McGill U., QC & Dr. Pablo Arroyo, NRC, Government of Canada) [ view full abstract ]

Session

SS6 » Summer School 6 (15:30 - Monday, 19th June, SH-3620)