Content

The course consist of three main parts:

In the first part, we introduce the general principles of remote sensing. We study the physical foundation of remote sensing, energy sources and radiation principles, energy interactions in the atmosphere and with earth surface features, data acquisition and interpretation concepts. We introduce common sensor and platform technology. 

In the second part, we overview image and signal processing techniques for mapping urbanization dynamics and to characterize urban land cover using various remote sensing data acquisition technologies. Advanced classification methods, including subpixel and object-based classifiers and machine learning approaches, are discussed.  

In the third part, we study the interaction of electromagnetic radiation (from visible light through microwave radiation) with plants, soil, water, atmosphere and construction/building materials. This fundamental knowledge about the interaction between light and matter is crucial to advance our understanding of remote monitoring of the urban environment. We focus on monitoring urban green space by introducing advanced sensing technology (e.g. fluorescence) and image processing techniques such as radiative transfer modelling, and spectral vegetation indices. We discuss how remote sensing can be supportive of urban green management (e.g. functional green type mapping, tree vitality monitoring). We further study how remote sensing can support the monitoring and modelling of the urban climate. We introduce sensor technology (e.g. thermal sensing) and methods to quantify urban heat fluxes, air quality and urban flooding. We conclude the course with a discussion on how remote sensing can support urban ecosystem analysis, planning and management.

Course material

English course notes: Remote Sensing of the Urban Environment (B. Somers)

Reference works

• Techniques and Methods in Urban Remote Sensing (Q. Weng), ISBN: 1119307333, 9781119307334
• Remote Sensing of Vegetation: Principles, Techniques, and Applications (Jones & Vaughan), ISBN-13: 9780199207794
• Okujeni et al. (2018). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. REMOTE SENSING OF ENVIRONMENT, 216, 482-496. doi: 10.1016/j.rse.2018.07.011
• van der Linden et al. (2018). Imaging Spectroscopy of Urban Environments. Surveys in Geophysics, 1-18. doi: 10.1007/s10712-018-9486-y
• Chi, D. et al. (2020). Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy. Remote Sensing, 12 (15), 2435-2435. doi: 10.3390/rs12152435
• Degerickx et al. (2020). Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data. SUSTAINABILITY, 12 (5), Art.No. ARTN 2144. doi: 10.3390/su12052144
• Soergel U. (2010) Review of Radar Remote Sensing on Urban Areas. In: Soergel U. (eds) Radar Remote Sensing of Urban Areas. Remote Sensing and Digital Image Processing, vol 15. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3751-0_1

Language of instruction: more information

• The lecturers teach in English
• The lecturers use course material in English
• The course material includes examples from urban areas around the world

Format: more information

This course is offered to the students in the form of topic-specific lectures with linked laboratory exercises (via class contact and online teaching)