Satellites could soon map every tree on Earth

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An analysis of satellite images has pinpointed individual tree canopies over a large area of West Africa. The data suggest that it will soon be possible, with certain limitations, to map the location and size of every tree worldwide.

Terrestrial ecosystems are defined in large part by their woody plants. Grasslands, shrublands, savannahs, woodlands and forests represent a series of gradations in tree and shrub density, from ecosystems with low-density, low-stature woody plants to those with taller trees and overlapping canopies. Accurate information on the woody-vegetation structure of ecosystems is, therefore, fundamental to our understanding of global-scale ecology, biogeography and the biogeochemical cycles of carbon, water and other nutrients.

Writing in Nature, Brandt et al.1 report their analysis of a massive database of high-resolution satellite images covering more than 1.3 million square kilometres of the western Sahara and Sahel regions of West Africa. The authors mapped the location and size of more than 1.8 billion individual tree canopies; never before have trees been mapped at this level of detail across such a large area.

The spatial resolution of most satellite data is relatively coarse, with individual image pixels generally corresponding to areas on the ground that are larger than 100 square metres, and often larger than one square kilometre. This limitation has forced researchers in the field of Earth observation to focus on measuring bulk properties, such as the proportion of a landscape covered by tree canopies when viewed from above (a measurement known as canopy cover).

However, during the past two decades, a variety of commercial satellites have begun to collect data at a higher spatial resolution, capable of capturing ground objects measuring one square metre or less. This resolution improvement places the field of terrestrial remote sensing on the threshold of a fundamental leap forward: from focusing on aggregate landscape-scale measurements to having the potential to map the location and canopy size of every tree over large regional or global scales. This revolution in observational capabilities will undoubtedly drive fundamental changes in how we think about, monitor, model and manage global terrestrial ecosystems.

Brandt et al. provide a striking demonstration of this transformation in terrestrial remote sensing. The authors analysed more than 11,000 images, at a spatial resolution of 0.5 m, to identify individual trees and shrubs with canopy diameters of 2 m or more. The authors completed this giant task using artificial intelligence — exploiting a computational approach that involves what are called fully convolutional neural networks. This deep-learning method is designed to recognize objects (in this case, tree canopies) on the basis of their characteristic shapes and colours within a larger image.

Convolutional networks rely on the availability of training data, which in this case consisted of satellite images in which the visible outlines of tree and shrub canopies were manually traced. Through training using these samples, the computer learnt how to identify individual tree canopies with high precision in other images. The result is a wall-to-wall mapping of all trees larger than 2 m in diameter across the whole of southern Mauritania, Senegal and southwestern Mali.



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