“Our objective is to see how much carbon is in isolated trees in the vast arid and semi-arid portions of the world”

preservation, restoration, climate change, and other purposes, data like these are very important to establish a baseline. In a year or two or ten, the study could be repeated with new data and compared to data from today, to see if efforts to revitalise and reduce deforestation are effective or not. It has quite practical implications.’ After gauging the program’s accuracy

by comparing it to both manually coded data and field data from the region, the team ran the program across the full study area. The neural network identified more than 1.8 billion trees – surprising numbers for a region often assumed to support little vegetation. ‘Future papers in the series will build on the foundation of counting trees, extend the areas studied, and look at ways to calculate their carbon content,’ said Tucker. Nasa missions such as the Global Ecosystem Dynamics Investigation mission, or Gedi, and IceSat-2 (Ice, Cloud and Land Elevation Sat-2), are already collecting data that will be used to measure the height and biomass of forests. In the future, combining these data sources with the power of AI could open up research possibilities, and help researchers more accurately calculate carbon sinks. ‘Our objective is to see how much

that grow individually or in small clusters. The team ran a powerful computing algorithm – a fully convolutional neural network – on Blue Waters. The team trained the model by manually marking nearly 90,000 individual trees across a variety of terrain, then allowing it to ‘learn’ which shapes and shadows indicated the presence of trees. The process of coding the training

data took more than a year, said Brandt, who marked all 89,899 trees himself and helped supervise training and running the model. Ankit Kariryaa, of the University of Bremen, led the development of deep learning computer processing. ‘In 1km of terrain, say it’s a desert, many times there are no trees, but the program wants to find a tree,’ Brandt said. ‘It will find a stone and think it’s a tree. Further | @scwmagazine

south, it will find houses that look like trees. It sounds easy, you’d think – there’s a tree, why shouldn’t the model know it’s a tree? But the challenges come with this level of detail. The more detail there is, the more challenges.’ Establishing an accurate count of trees in this area provides vital information for researchers, policymakers and conservationists. Additionally, measuring how tree size and density vary by rainfall – with wetter and more populated regions supporting more and larger trees – provides important data for on-the- ground conservation efforts. ‘There are important ecological

processes, not only inside, but outside forests too,’ said Jesse Meyer, a programmer at Nasa Goddard who led the processing on Blue Waters. ‘For

carbon is in isolated trees in the vast arid and semi-arid portions of the world,’ Tucker said. ‘Then we need to understand the mechanism which drives carbon storage in arid and semi-arid areas. Perhaps this information can be utilised to store more carbon in vegetation by taking more carbon dioxide out of the atmosphere.’ Brandt said: ‘From a carbon-cycle

perspective, these dry areas are not well mapped, in terms of what density of trees and carbon is there. It’s a white area on maps. These dry areas are basically masked out. This is because normal satellites just don’t see the trees – they see a forest, but if the tree is isolated, they can’t see it. Now we’re on the way to filling these white spots on the maps. And that’s quite exciting.’

Winter 2021 Scientific Computing World 17

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