Remote Sensing of Vesuvius wildfires

Satellites are circling our planet, allowing us to "sense" things about the Earth. It is the Art and Science of making measurements using sensors.

Remote sensing has thus become a valuable tool in research and applications in a wide range of disciplines, such as engineering, geology, geography, urban planning, public health, archeology, environmental studies, disaster research, forestry, and agriculture.

In this project, we embark on a journey to explore satellite imagery, particularly focusing on Earth observations before and after the Mount Vesuvius (Naples, Italy) 2017 fires. [ESA Multimedia] [Local news (in Italian)]

Huge clouds of smoke drift over Naples from fires on Mount Vesuvius (AP Images)

We aim to:

• leverage fundamental principles of remote sensing methods employed in research;
• harness the Google Earth Engine's Python API to work with Sentinel-2 imagery;
• perform a basic analysis of Normalized Difference Vegetation Index (NDVI) to assess vegetation health and changes over time.

How bad is the situation?

The image below is the result from the analysis of satellite data from a few weeks before and after the wildfires, as well as about 6 years later.

RGB composite and NDVI pre- and post-wildfires

In the top row is the RGB composite, while in the bottom row is the Normalized Difference Vegetation Index (NDVI).

The latter is defined as

$$\rm{NDVI \equiv \frac{NIR - RED}{NIR + RED}}$$

and it measures greenness and density of the vegetation captured in a satellite image, allowing the immediate recognition of areas that have problems.

Vegetation index explained

The color palette identifies areas with healthy vegetation in green, while areas with gradually more damaged vegetation are identified in yellow and red.

The evolution of NDVI shows the tremendous damage caused by fires at Vesuvius National Park. However, 6 years after its devastation the vegetation appears to be on track for healing.

How can I run the code myself?

• First, in order to execute the code, you need a Google Earth Engine account, which can be obtained here

• If you want to run the code locally, you need to clone the repository and install dependencies, preferably via poetry with poetry install

• To launch a web server and execute the jupyter notebook:

  • you can run the scripts/run-jupyter.ps1 script (on Windows); otherwise, you can activate the virtual environment manually (via poetry shell) and then execute the poetry run jupyter notebook command

  • otherwise, if you do not intend to install the project locally, please find a badge at the top to spin-up a Docker container and interact with the notebook in a live environment via Binder (⚠️ Launching Binder will take 2-5 minutes)