Extracting satellite data for areas of interest using the Climate Engine app

Satellite indices are powerful datasets for investigating patterns and changes in vegetation productivity, surface water, wildfire severity, surface temperature, and other important environmental processes. And these types of data can be used to perform entire analyses, or as ancillary datasets alongside local information.

No matter how you want to use satellite data, Climate Engine can help make it easier. Climate Engine is being applied by users across the globe for this purpose and in this video we’ll demonstrate how you can perform custom analysis for your areas of interest quickly and easily.

In this video, we demonstrate uploading a shapefile, discuss core considerations for extracting timeseries (selecting a dataset, time period, etc.), show both the native and summary time series functions, and demonstrate how data can be exported out of Climate Engine.

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Chapters:
0:00 Opening
0:44 Accessing the Climate Engine App
1:20 Description of the example and generating NDVI map of study area
2:56 Extracting NDVI for our area of interest | Native timeseries
5:35 Extracting NDVI for our area of interest | Summary timeseries
6:32 Closing

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Learn more about all of our tools and efforts at https://climateengine.org!

Visit the Climate Engine web application at https://app.climateengine.org.

Here are links to our introductory tutorials to the Climate Engine web application:
- Making Maps in the Climate Engine Research App:    • Making Maps in the Climate Engine Res...  
- Creating Graphs in the Climate Engine Research App:    • Creating Graphs in the Climate Engine...  

The ClimateEngine.org Support Site can be found at https://support.climateengine.org.

Here are links to the datasets and indices referred to in the video:
- Landsat 5/7/8/9: https://support.climateengine.org/art...
- Normalized difference vegetation index (NDVI): https://support.climateengine.org/art...
- Climate Engine metrics and indices: https://support.climateengine.org/art...

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#RemoteSensing #Climate #ClimateChange #LandManagement #DataScience #DataVisualization #Mapping #geospatial

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🎵 Music credits 🎵
Luminous Trails, PineAppleMusic
Tutorial, Paul Yudin