Short biography

Miguel Kohling

My work focuses on quantitative analysis of land use and land cover by image classification and machine learning algorithms. I have gathered broad knowledge in analytics of data from active (e.g. SAR, LiDAR) and passive (e.g. multi- and hyperspectral optical) air and spaceborne sensor systems. My experience encompasses big data processing and automation for county-wide assessments based on remotely sensed data, as well as related spatial statistics. Furthermore I have planned and conducted field campaigns for in-situ data collection. In the role as researcher, freelancer and consultant, I have collaborated with international, governmental and research institutions in Germany and abroad. As a member of the Earth Observation of Agroecosystems Team at Agroscope I am currently investigating the detectability and quantification of artificial irrigation of agricultural plots by means of multi-spectral optical and SAR satellite data and appropriate machine learning techniques. Furthermore I am contributing to the development of an in-house remote sensing software package based on Python.