Hyperspectral Remote Sensing

field spectroscopy and image analysis

Lecturer

Martin Bachmann

ECTS

5 ECTS

 

Aim

Spectroscopy and hyperspectral remote sensing enables to retrieve very detailed spectral information about a certain surface in dense bandwith intervalls. Information on the “spectral fingerprints” of surfaces is then available in a near-continuous manner. This allows for the differentiation of materials, such different geologic surfaces, different urban materials, or plants of different composition and vigor. Especially field- and laboratory spectroscopy has shown many benefits, as measurements can be carried out in a controlled environment, and can be directly visualized and explained. This course provides you insights into practical experiments using a field spectrometer, and subsequent data analysis to assess key environmental parameters such as plant health, soil moisture content, and geologic composition.

 

Content

The content of this course includes both the theoretical background of field and imaging spectroscopy, as well as practical experiments and subsequent data analysis. In particular, we will adress: the theoretical background of field and imaging spectroscopy / general reflectance and transmittance properties of plant leaves, canopies and soils / the quantification of biophysical and biochemical properties using spectroscopic measurements, feature parametrization and regression analysis / the advantages and challenges of existing and planned hyperspectral spaceborne sensors

 

Coding

Coding examples and individual work will be covered

Software

Various software programs will be used, but mainly OpenSource software such as R.

Techniques

Different techniques will be introduced and practically applied.

w

Content

The content of scientific with regard to the audience will be discussed.

General Course News and Updates

EAGLE students coding with sweets

Today our EAGLE students applied data munging, pipes, plotting and statistics using colour distribution of sweets. They specifically used the dplyr, ggplot, kableExtra and others to compute derivatives, rearrange the data, plot it and run statistics on colour...

read more

Spatial Python block course

Last week Steven Hill and Thorsten Dahms gave a course that introduced EAGLE students to Python-based spatial data analysis. The advantages and challenges of different python libraries, data sets and methods were covered in hands-on exercises and also discussed...

read more
Share This