Digital Projects Showcase: Google Earth Engine
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Google Earth Engine is a powerful geospatial analysis tool that allows end users to leverage the power of the Cloud to work with big data in remote sensing. While lesser known than its cousin Google Earth, Earth Engine has many advantages for prospective users beyond data visualization including: data manipulation, statistical analysis, and development of custom tools for project specific tasks. Earth Engine maintains a wealth of public datasets for users to work with such as Landsat imagery, Digital Elevation Models, and high-resolution aerial imagery. As part of a USDA funded research project at Middle Tennessee State University, Google Earth Engine was used to collect and process hundreds of Landsat images. These images were used to measure crop growth curves which will aid in the development of crop-land-cover GIS datasets for years in which data is unavailable to stakeholders.
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The document summarizes the web mapping activities of the Massachusetts Historical Commission (MHC). The MHC uses web mapping to facilitate exploring and accessing its inventory of historic and archaeological sites. It maintains a web GIS allowing users to view, query, and export data from its inventory alongside other datasets. The MHC has improved its web GIS over time, moving to open source technologies like OpenLayers to make the system faster, more flexible, and under its own control.
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Digital Projects Showcase: Google Earth Engine
- 1. November 29, 2017 Google Earth Engine Wes Porter | H. Momm
- 2. What is Google Earth Engine? “A web browser platform for geospatial data processing and analysis.” 2
- 3. 3 Google Earth Google EngineVs.
- 4. Google Earth Data Visualization • Virtual Globe • 3D Cities • Street View Google Engine • Spectral Transformations • Cumulative Cost Mapping • Time lapses • Edge Detection Data Analysis 4
- 5. Timelapse 5
- 6. Advantages • Track, measure, visualize changes over time • Extensive open data catalogue • Customizable 6
- 7. Your data lives here. Not on your computer! 7https://www.google.com/about/datacenters/gallery/#/all/20
- 8. 8
- 9. 9
- 10. Measuring Crop Growth from NDVI Temporal Image Stack 10
- 11. Crop Specific Growth Curves 16
- 12. End Product 17 2015 CDL – S.A.M. Undetermined Corn Soybeans Wheat Study Fields 0 0.25 0.5 1 Mile N
- 13. • Future Research / Next Steps - Generate crop data layers for years in which USDA data is unavailable • Why consider Google Earth Engine? - It’s free - It’s powerful - Mountain of Earth observation data to play with 19 Closing Thoughts
- 14. Questions? Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. Beis, Jeff. (2017). Earth Engine Demo: Trendy Lights. github.com/google/earthengine-api/demos/trendy-lights Satellite by Sergey Demushkin from the Noun Project Images from earthobservatory.nasa.gov Acknowledgements This project was partially supported by funding from the Space Grant Consortium and USDA – National Institute of Food and Agriculture (2017-70001-25989) with an appointment to a graduate research assistance ship through the College of Graduate Studies at Middle Tennessee State University. Special thanks to Dr. Henrique Momm for his mentorship and support. Wes Porter wsp2s@mtmail.mtsu.edu @Wes_Port
Editor's Notes
- Google Earth Engine – A geospatial dark horse: -Little known yet powerful capabilities -The best part - It’s free! - western australia salt ponds
- History: Passion project (20% time) of Rebecca Moore (Google Earth Director) Born out of desire to help poorer nations monitor deforestation Google worked w/ Geographic Information Science center at South Dakota State and Imazon Launched on Dec. 2, 2010 at the International Climate Change Center in Cancun Mexico 32 years of data 2,068,467 Landsat scenes 909 terabytes of data
- - Google Engine is a separate geospatial analysis product
- These are just a few of the available functions on GEE The strength of GEE is its FLEXIBILITY!
- Notice the housing boom in West Murfreesboro? Appearance of 840 Construction of the Avenue, Medical Center
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- Mayes County, Oklahoma
- - Custom application made with the Python API - Linear regression of brightness over time
- NDVI = Normalized Difference Vegetation Index i.e. how healthy is the crop? NDVI = (NIR – RED) / (NIR +RED) Each image is a snapshot in time
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- - Integrate GEE with other workflow tools such as ENVI and ArcMap
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