作者: G. Kereszturi , L.N. Schaefer , W.K. Schleiffarth , J. Procter , R.R. Pullanagari
DOI: 10.1016/J.JAG.2018.07.006
关键词:
摘要: Abstract Optical and laser remote sensing provide resources for monitoring volcanic activity surface hydrothermal alteration. In particular, multispectral hyperspectral imaging can be used detecting lithologies mineral alterations on the of actively degassing volcanoes. This paper proposes a novel workflow to integrate existing optical data geological mapping after 2012 Te Maari eruptions (Tongariro Volcanic Complex, New Zealand). The image classification is based layer-stacking features (optical textural) generated from high-resolution airborne imagery, Light Detection Ranging (LiDAR) derived terrain models, aerial photography. images were classified using Random Forest algorithm where input added multiple sensors. Maximum accuracy (overall accuracy = 85%) was achieved by adding textural information (e.g. mean, homogeneity entropy) LiDAR data. returned total alteration area ∼0.4 km2 at Maari, which confirmed field work, lab-spectroscopy backscatter electron imaging. Hydrothermal volcanoes forms precipitation crusts that mislead classification. Therefore, we also applied spectral matching algorithms discriminate between fresh, crust altered, completely altered rocks. confidently recognized areas with only alteration, establishing new tool structurally controlled evolving debris flow eruption hazards. We show fusion remotely sensed automated map significantly benefit understanding processes their