Data in the Wild! Normalized Difference Vegetation Index Temporal Analysis of Mole National Park Using Deep Learning and Satellite Data

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Data in the Wild! Normalized Difference Vegetation Index Temporal Analysis of Mole National Park Using Deep Learning and Satellite Data

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    By Ditiro Rampate

    Botswana OmdenaAI Chapter Co-Lead/ Member @ Black Python Devs/Organiser @ SisonkeBiotik,Deep Learning IndabaX Botswana, Deep Learning IndabaX South Africa at Black Python Devs/SisonkeBiotik/ OmdenaAI

    Abstract:

    In this workshop, we’ll embark on a data-driven journey through Mole National Park. The workshop is suited for intermediate or advanced users who have (basic) skills in Python, image processing and time series . You will learn how to perform temporal analysis of the Normalized Difference Vegetation Index (NDVI) using deep learning and satellite data. We will start with a concise introduction to remote sensing. Then, we will cover the basics of Earth Engine data types and how to analyze, and export Earth Engine NDVI data in a Jupyter environment using geemap and Google Earth Engine.Afterwards we’ll start utilizing Google Earth Engine for collecting historical NDVI values from satellite images, analyzing NDVI trends, and forecasting future NDVI patterns.

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