Читать книгу Earth Observation Using Python - Rebekah B. Esmaili - Страница 21

I have structured this book so that you can learn Python through a series of examples featuring real phenomena and public datasets. Some of the datasets and visualizations are useful for studying wildfires and smoke, dust plumes, and hurricanes. I will not cover all scenarios encountered in Earth science, but the skills you learn should be transferrable to your field. Some of these case studies include:

 California Camp Fire (2018). California Camp Fire was a forest fire that began on November 8, 2018, and burned for 17 days over a 621 km2 area. It was primarily caused by very low regional humidity due to strong gusting wind events and a very dry surface. The smoke from the fire also affected regional air quality. In this case study, I will examine satellite observations to show the location and intensity as well as the impact that the smoke had on regional CO, ozone, and aerosol optical depth (AOD). Combined satellite channels also provide useful imagery for tracking smoke, such as the dust RGB product. Land datasets such as the Normalized Difference Vegetation Index (NDVI) are useful for highlighting burn scars from before and after the fire events.

 Hurricane Michael (2018). Michael was a major hurricane that affect the Florida Panhandle of the United States. Michael developed as a tropical wave on October 7 in the southwest Caribbean Sea and grew into a Category 5 storm by October 10. Throughout its life cycle, Michael caused extensive flooding, leading to 74 deaths and $25 billion in damage. Several examples in this text utilize visible and infrared imagery of Hurricane Michael.

 Louisiana Flood Event (2016). Thousands of homes were flooded in Louisiana when over 20 inches of rain fell between August 12 and August 21, 2016. The event began after a mesoscale convective system stalled over the area near Baton Rouge and Lafayette, Louisiana. I will use the IMERG global rainfall dataset to examine this event.