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

光学和激光遥感为监测火山活动和地表热液变化提供了资源。特别地，多光谱和高光谱成像可用于检测活跃脱气的火山表面上的岩性和矿物蚀变。本文提出了一种新颖的工作流程，将2012年Te Maari火山喷发（Tongariro火山群，新西兰）后整合现有的光学和激光遥感数据用于地质测绘。图像分类基于从高分辨率机载高光谱图像，光检测和测距数据（LiDAR）派生的地形模型以及航空摄影生成的图像特征（光学和纹理）的层堆叠。使用随机森林算法对图像进行分类，其中从多个传感器添加输入图像。通过在高光谱和LiDAR数据中添加纹理信息（例如均值，均一性和熵），可以实现最大的图像分类精度（总体精度= 85％）。该工作流程返回了约0.4 km的总表面变化面积蒂马里（Te Maari）的第^2位科学家通过现场工作，实验室光谱学和反向散射电子成像得到了证实。火山上的热液蚀变会在地表形成沉淀壳，这可能会误导图像分类。因此，我们还应用光谱匹配算法来区分新鲜的，地壳改变的和完全改变的火山岩。该工作流程仅凭表面变化就能自信地识别区域，从而建立了一种新的工具，用于绘制结构受控的热液变化，不断发展的泥石流和热液喷发危害的地图。我们表明，遥感数据的数据融合可以自动绘制火山图，并极大地帮助人们理解火山过程及其危害。