The results of a research project on the mapping of trees in West Africa can help to strengthen ecosystems.
An international team of researchers recently made the efficient mapping and counting of individual trees in large areas possible for the first time ever. The core was an artificial intelligence (AI) process, which was made to fit for this particular purpose and trained by scientists from the Center for Computing Technologies (TZI).
The success of the project suggests that it may soon be possible to map all trees on Earth – with minimal restrictions.
The researchers mapped every tree and bush with a crown that covered a minimum surface of three square meters. They did this across 1.3 million square kilometers in West Africa. The result: Around 1.8 billion individual trees can be found in the West African Sahara and the adjacent Sahel region – far more than assumed to date.
The gained data could help to strengthen ecosystems, gain data for climate protection, and observe the deforestation processes, amongst other things.
The identification of individual trees was made possible due to the fact that NASA and private space travel companies continually provide more and more high-definition photo material. In order to analyze the masses of data, the TZI scientists Professor Johannes Schöning and Ankit Kariryaa adapted an AI process from the field of Deep Learning – namely fully convolutional neural networks.
The chosen AI process can recognize objects – for example treetops – based on their characteristic colors and shapes. The AI system was trained with the help of images in which the trees had been marked by hand.
The result, published in Nature, of this work is a map showing all trees that have a diameter of two meters or above in the South of Mauretania, Senegal, and Mali. In the future, the results can not only be expanded geographically to include of regions of the world, but also be combined with additional data, such as radar sensors. This could help to determine different tree types, for example.
Image credit: Martin Brandt
