An example of comparing changes in imagery over time in Dubai. This notebook explores ways to collect several images, compute water indices from each, extract vector features, and compare changes to land area over time.
This notebook is the third in a series of beginner tutorial to introduce news users to Jupyter Notebooks, working with satellite imagery and performing basic analytics. Specifically this notebook introduces the concepts of multispectral bands found in satellite imagery and how they can be used for analytics using "band math".
In this tutorial we'll cover how to find SWIR imagery and use it for creating a Normalized Difference Built-Up Index for analysis of Central Park in Manhattan. This work includes discussion of the difference between VNIR and SWIR data as well as using downsampling techniques to enable the use of band math on imagery products with different resolutions.
This notebook provides a basic introduction to using Dask with satellite imagery. It covers a brief overview of Dask arrays, chunks and map_blocks to scale out an NDVI analysis for a entire strip of imagery over Sydney, Australia.