Non-Forest Tree Count AI using Satellite Data

Non-Forest Tree count ~ using new Artificial Intelligence (AI) for calculating non-forest trees across multiple Climate Zones using satellite data for input.   

Mapping non-forest trees at this level of detail would take months or years with traditional analysis methods, the team said, compared to a few weeks for this study. The use of very high-resolution imagery and powerful artificial intelligence represents a technology breakthrough for mapping and measuring these trees. This study is intended to be the first in a series of papers whose goal is not only to map non-forest trees across a wide area, but also to calculate how much carbon they store – vital information for understanding the Earth’s carbon cycle and how it is changing over time.

The team focused on the dryland regions of West Africa, including the arid south side of the Sahara Desert, stretching through the semi-arid Sahel Zone and into the humid sub-tropics. By studying a variety of landscapes from few trees to nearly forested conditions, the team trained their computing algorithms to recognize trees across diverse terrain types, from deserts in the north to tree savannas in the south. Credit: NASA’s Scientific Visualization Studio; Blue Marble data is courtesy of Reto Stockli (NASA/GSFC)

To learn more:

https://scitechdaily.com/nasa-uses-powerful-supercomputers-and-ai-to-map-earths-trees-discovers-billions-of-trees-in-west-african-drylands/ 

NASA Uses Powerful Supercomputers and AI to Map Earth’s Trees, Discovers Billions of Trees in West African Drylands

TOPICS:  Forests, NASA, NASA Goddard Space Flight Center, Trees

By JESSICA MERZDORF, NASA’S GODDARD SPACE FLIGHT CENTER, NOVEMBER 22, 2020

Scientists from NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and international collaborators demonstrated a new method for mapping the location and size of trees growing outside of forests, discovering billions of trees in arid and semi-arid regions and laying the groundwork for more accurate global measurement of carbon storage on land.

Using powerful supercomputers and machine learning algorithms, the team mapped the crown diameter – the width of a tree when viewed from above – of more than 1.8 billion trees across an area of more than 500,000 square miles, or 1,300,000 square kilometers. The team mapped how tree crown diameter, coverage, and density varied depending on rainfall and land use. 

Mapping non-forest trees at this level of detail would take months or years with traditional analysis methods, the team said, compared to a few weeks for this study. The use of very high-resolution imagery and powerful artificial intelligence represents a technology breakthrough for mapping and measuring these trees. This study is intended to be the first in a series of papers whose goal is not only to map non-forest trees across a wide area, but also to calculate how much carbon they store – vital information for understanding the Earth’s carbon cycle and how it is changing over time.