Optical remote sensing enables the deduction of various vegetation-related characteristics, including biochemical properties (e.g., pigments, water content), structural properties (e.g., leaf area index (LAI), biomass) or process properties (e.g., light use efficiency (LUE)). The ability to deduce these characteristics depends on the ability of a sensor to resolve vegetation spectra. Hyperspectral sensors capture spectral information in hundreds of narrow and contiguous bands in the VIS, NIR and SWIR, and, thus, resolve subtle absorption features caused by specific vegetation constituents (e.g. anthocyanins, carotenoids, lignin, cellulose, proteins). In contrast, multispectral sensors capture spectral information in a few broad spectral bands and, thus, only resolve broader spectral features. Still, multispectral systems like Sentinel-2 have been demonstrated to be useful to derive valuable vegetation properties (e.g., LAI, chlorophyll).
A vegetation index (VI) represents a spectral transformation of two or more bands of a multispectral image into a singleband image. A VI is designed to enhance the vegetation signal with regard to different vegetation properties, while minimizing confounding factors such as soil background reflectance, directional, or atmospheric effects. There are many different VIs, including multispectral broadband indices as well as hyperspectral narrowband indices.
Most of the multispectral broadband indices make use of the inverse relationship between the lower reflectance in the red (through chlorophyll absorption) and higher reflectance in the near-infrared (through leaf structure) to provide a measure of greenness that can be indirectly related to biochemical or structural vegetation properties (e.g., chlorophyll content, LAI). The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used broadband VIs:
\[NDVI = (ρNIR- ρRED) / (ρNIR+ ρRED)\] The NDVI calculation results in an singleband grayscale image, with a potential range of NDVI values between -1 and 1.
In case you are interested in exploring more spectral indices please see this exhaustive list of indices.
Please download the session materials from our shared repository. The materials contain the prepared Sentinel-2 image from last week (acquisition date 26.07.2019, hereafter called summer image) and a second prepared Sentinel-2 scene taken in winter (acquisition date 16.02.2019, hereafter called winter image). Further, the session materials contain an imperviousness map provided by the Copernicus Services capturing the percentage of soil sealing.
Open the EnMAP-Box and display the Sentinel-2 summer image in an RGB combination of your choice.
Open the application imageMath and calculate the NDVI.
Display the NDVI as grayscale image in a second map.
Use the Identify cursor location values functionality as well as the Image statistics application to answer the following questions:
The aim of this assignment is to gain a deeper understanding of the information content provided by the NDVI by assessing the relationship between the NDVI and impervious cover, and by assessing the temporal variation of NDVI values through a bi-seasonal summer vs. winter comparison
Please upload the comparison between NDVI and imperviousness (scatterplot + bullets) as well as the bi-seasonal NDVI comparison (histogram + bullets, map + bullet) as pdf to moodle.
General submission notes: Submission deadline for the weekly assignment is always the following Monday at 10am. Please use the naming convention indicating session number and family name of all students in the respective team, e.g. ‘s01_surname1_surname2_surname3_surname4.pdf’. Each team member has to upload the assignment individually. Provide single file submissions, in case you have to submit multiple files, create a *.zip archive.
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