Andres Schmidt

As a physical geographer with a background in complex data analysis and spatial modeling,  I am interested in the exploration and analysis of the ever-increasing richness of data from remote sensing projects or measurement networks including species distributions, land cover and land use information, forest fire fuel repositories, and long-term climate data. My research focuses on the application of process models and machine learning methods, in particular neural networks, to analyze and model a variety of bio-geophysical processes associated with a changing climate reaching from alterations of atmosphere-land exchange to wildfire risk assessments.