12th EARSeL Imaging Spectroscopy Workshop - Part I: Introduction into the EnMAP-Box

Authors: Andreas Rabe

Date: 17/06/2022

EnMAP-Box 3: version 3.10

QGIS: version 3.24


In this tutorial we will show the basic components of the EnMAP-Box. Participants will learn how to manage, visualize and process raster and vector data inside the EnMAP-Box. We will detail the multiple-viewer concept of the EnMAP-Box, that allows the exploration of data in different visualizations at the same time. Here we will especially highlight, how we capitalize on the spectral characteristics of optical (hyperspectral) EO data, e.g. by correctly plotting pixel profiles against the given center wavelength, or by quickly selecting target bands for visualization. Finally, we will perform a Classification Analysis Workflow using PRISMA or (simulated) EnMAP data to illustrate EO data processing.
This tutorial requires version 3.10+ of the EnMAP-Box 3 running in QGIS 3.24+.

You can download the data for this exercise here: https://box.hu-berlin.de/f/2ecf8943d6fa4474bf38/?dl=1

The tutorial dataset contains a PRISMA L2D image over Berlin, Germany, as well as a point based landcover reference sample.

Type Filename Description
Raster PRS_L2D_STD_20201107101404_20201107101408_0001_SPECTRAL.tif Surface reflectance, coregistersed, hyperspectral cube from the PRISMA sensor with a spatial resolution of 30m, 234 bands, and 1281x1238 pixels
Vector landcover_berlin_point.gpkg 58 landcover reference points

For more details on PRISMA data see: https://prisma.asi.it/

Exercise A: Visualize PRISMA data inside the EnMAP-Box

Open the PRISMA raster

In the EnMAP-Box, open the PRS_L2D_STD_20201107101404_20201107101408_0001_SPECTRAL.tif raster and visualize it in a map view using drag&drop.

Style the PRISMA layer using an RGB visualization

We can improve the visualization using the Raster Layer Styling panel.

As the PRISMA layer contains information about the center wavelength location for each band, we can directly choose from a list of predefined RGB band combinations, e.g. Natural color (R-G-B) for showing Red band, Green band and Blue band as an RGB visualization, or Agricultural 1 (S1-N-B) for showing SWIR 1 band, NIR band Blue band as an RGB visualization.

Use the A, B, …, S2 buttons, to select individual PRISMA bands that best match the predefined broad bands from the Sentinel-2 sensor, e.g. N is associated with the Sentinel-2 NIR band at 833 nanometers.

Use the widgets in the Min / Max Value Settings to interactively alter and improve the contrast stretch.

Use a singleband gray visualization to identify bad bands

Due to atmospheric conditions, the PRISMA raster may contain bad bands, which can be identified easily using a singleband gray visualization.

In the Raster Layer Styling panel select the Gray tab to change the visualization.

Use the band selection slider or the band selection combobox (together with the Arrow-Up and Arrow-Down keys) to browse the PRISMA bands and identify potential bad bands.

As before, you can also use the A, B, …, S2 buttons.

Plot spectral profiles and show cursor location values

Use the Identify map tool to plot PRISMA spectral profiles and query band values for the selected location.

In the Spectral Profile Sources panel you can easily alter the plot style.

Exercise B: Perform a land cover classification

Derive a landcover map for the PRISMA raster

Use the Classification workflow algorithm to perform a full landcover classification. This includes training data preparation, fitting a Random Forest Classifier, and predicting landcover classes and class probabilities.

The original PRISMA raster and the derived classification and class probabilities can be visualized next to each other using multiple map views.

Finally, visualize the probabilities for the classes impervious, tree and water as RGB multiband color.

Visualize the predicted maps

Use the Classification Statistics app to visualize the derived classification map.

Use the Class Fraction/Probability Statistics app to visualize the derived class probability map.

View the fitted Random Forest Classifier model

The training data and the fitted model can be explored inside the Data Sources panel.