a. Definition, historical background, and applications

b. Active vs. passive sensing, remote sensing platforms

a. Electromagnetic spectrum and key sections for remote sensing

b. Properties of waves: wavelength, frequency, reflection, absorption

a. Atmospheric composition and effects on radiation

b. Scattering, absorption, and atmospheric windows

a. Reflection, refraction, and spectral characteristics of surfaces (vegetation, water,           soil)

b. Thermal emission and blackbody radiation

a. Types of sensors

b. Data acquisition methods and remote sensing systems

a. Pre-processing

b. Image classification and change detection

a. Environmental monitoring → Strong emphasis on wetlands 

a. Overall aim and focus on Sentinel-2 data and their vegetation/landcover analysis

b. Importance of ancillary data (in the following mostly “vector data”)

c. Importance of field / in situ data

a. Sentinel-2 images – spectral, temporal and spatial resolution

b. Possibilities and limitations of Sentinel-2 data

a. Formats, types, file types, metadata, sources

a. Vector import/export

b. Raster import/export

a. Vector manipulations

b. Projections

a. Buffer, intersect, query

a. Special focus on clouds and creating mosaics

a. Vegetation indices (NDVI, NDMI, NDWI, …): suitability and calculation

b. Land cover classification (supervised vs. unsupervised classification algorithms)

a. Combination of remote sensing products, ancillary data and field data