This visualization shows “an unexpectedly large count of trees in the West African Sahara and Sahel” which is also the title of the paper published in the 14-Oct-2020 issue of NATURE. Dryer areas of the globe that do not have forests are sometimes discounted for their contribution to the global biome in climate models and other prediction systems. However, this research has shown that over 1.8 billion trees are in the small study area in the West African Sahara and Sahel.
A deep learning algorithm was trained by the scientists to identify trees using very high resolution satellite imagery (0.5m per pixel) from DigitalGlobe. The algorithm identified trees by looking for appropriate colors and shadows cast. A total of 11,128 multispectral images were used to identify trees in this study region. Using this technique 1,837,565,501 trees were identified in the study region with a median tree crown area of 12 square meters.
The visualization starts at a global scale then pushes in to show the study area. To illustrate that this is a dry area, climate zones are shown using annual rainfall averages from 1982-2017 including regions that are:
hyper-arid (0-150 mm rainfall/year)
arid (150-300 mm/year)
semi-arid (300-600 mm/year)
sub-humid (600-1000 mm/year)
We then zoom in to a semi-arid area in Senegal down to a point where we can see individual trees.
The visualization next shows an area of high resolution imagery of the trees, then overlays the results of the machine learning which are filled regions of tree crowns for each tree in view. The trees are then counted up. The areas of trees are also totaled using the tree crown regions. We then zoom back out to see the entire study area and the total tree count and area.
This is a success story in using deep learning and big data to perform large scale scientific analysis. Now, scientists know the number and size of these trees in the Sahara and the Sahel. They will be able to use this information to calculate their impact to our planet.
Visualization showing study region, climate zones, close up of high res satellite data with machine learning-based tree crown regions, counting of trees, and overall tree counts and area (with French annotations). The native language in Senegal is French
GCMD keywords can be found on the Internet with the following citation:
Olsen, L.M., G. Major, K. Shein, J. Scialdone, S. Ritz, T. Stevens, M. Morahan, A. Aleman, R. Vogel, S. Leicester, H. Weir, M. Meaux, S. Grebas, C.Solomon, M. Holland, T. Northcutt, R. A. Restrepo, R. Bilodeau, 2013. NASA/Global Change Master Directory (GCMD) Earth Science Keywords. Version 188.8.131.52.0