# Learning goals

• Recap vegetation properties
• Get to know the Normalized Difference Vegetation Index (NDVI)
• Learn how to calculate the NDVI
• Understand the information content provided by the NDVI

# Background

## Vegetation properties

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).

## Vegetation Indices

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.

## NDVI

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.

# Session materials

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.

# Exercise

## Calculate NDVI

• 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.

## Interpret 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:

• What is the range of NDVI values of the entire image?
• What are typical NDVI values for asphalt, bare soil, water, broadleaf trees, needleleaf tree, and grass? What are the underlying reflectance values in the near-infrared and red band?
• Under which circumstances will NDVI values of exactly -1, 0, +1 be calculated?

## Visualize NDVI classes

• Visualize the NDVI image in three distinct classes: water, impervious/soil and vegetation via Layer properties > Symbology. Choose the class boundaries based on your previous findings

# Assignment

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

## NDVI vs. imperviousness

• Create a scatterplot that shows the relation between the NDVI and imperviousness. Use your calculated NDVI image as well as the ‘copernicus_imperviousness_2015_berlin.bsq’ image provided in the session materials.
• To create the scatterplot, extract and note at least 10 point pairs (i.e. pixel values) from both images and display their relationship as scatterplot using a spreadsheet software or R. Please make sure that the ranges of NDVI values (e.g. -0.2 to 1) and impervious values (0-100%) are well captured.
• Capture a screenshot of your scatterplot and briefly discuss the relationship between NDVI and imperviousness in a bullet list.

## Bi-seasonal NDVI

• Compare the temporal variation of NDVI values between summer and winter.
• Use the winter image (Sentinel2_T33UUU_20190216_20m_9bands_subset_berlin.bsq) provided in the session materials to calculate a second NDVI image for winter. Then calculate an NDVI difference image between both seasons (NDVI summer - NDVI winter). Both steps can be conducted in one script with the imageMath application.
• Capture a histogram of the difference-image and briefly discuss the distribution in a bullet list.
• Visualize the NDVI difference image as a map that highlights surfaces with large changes in NDVI between both seasons (e.g. through discrete classes or through color gradient from negative to positive differences).
• Capture a screenshot of your map (incl. legend) and discuss the map with regard to the classes below in a bullet list:
• Buildings & sealed non built-up
• Water
• Deciduous forests
• Coniferous forests
• Agriculture

## Submission

• 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.