Making Sense of Big Data

Assessing fire extent and fire severity with analysis ready Sentinel-2 data in the Open Data Cube python environment.

Differenced normalised burn ratio (dNBR). Contains modified Copernicus data (2020) processed by Digital Earth Australia. ©Copernicus data (2020).

In 2019 to 2020, Australia suffered through some of the most devastating fires ever seen on record. In the unfortunate events that occurred, 10.2 million hectares of land burned, 3100 homes were destroyed, 3 billion animals were killed, and 33 lives were lost [1]. The socioeconomic and ecological impacts of the fires however have extended well beyond the immediate loss. Assessing and analysing the events of the fires quantitively and qualitatively is key to informing policy and decision makers for recovery efforts.

The Open Data Cube initiative is an excellent example of an open source project specifically designed around unlocking…

Assessing the accuracy of fire severity and extent classifications using Python 3, GeoPandas and ‘normal’ Pandas with data derived from Planet and analysis ready data from Sentinel-2 through the Open Data Cube.

Amos J Bennett

Spatial science with a background in civil engineering, remote sensing and GIS. Python fanatic.

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