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NEWS AND VIEWS
14 October 2020

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.

Niall P. Hanan &

Niall P. Hanan is with the Jornada Basin Long-Term Ecological Research Program, Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico 88003, USA.
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Julius Y. Anchang

Julius Y. Anchang is in the Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico 88003, USA.
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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.

A previous estimate2 of the total number of trees on a global scale was obtained using field data from approximately 430,000 forest plots around the world. The authors of that study used statistical regression models to estimate tree density between the field sites, on the basis of vegetation type and climate. Their analysis suggested that there are approximately three trillion trees globally. However, this approach to tree-density estimation has inherent errors and uncertainties, particularly for drylands, for which relatively few field measurements are available to calibrate the models.
A comparison (Fig. 1) of that earlier result with Brandt and colleagues’ findings in the western Sahel, for example, shows that the previous study tended to underestimate the number of trees in the drier regions (areas with annual rainfall of less than 600 millimetres). Moreover, the previous estimates provided no information on the location and size of individual trees within each square kilometre, whereas Brandt and colleagues provide detailed information on the location and size of every individual canopy. The improvement provided in the latest study can also be seen in the much higher level of detail it gives for the wetter regions (those with annual rainfall greater than 600 mm), and shows local spatial variability in trees that is presumably associated with contrasting soil types, water availability, land use and land-use history.

Figure 1 | Large-scale tree mapping. Accurate information about tree distribution provides useful ecological insights, but such data are difficult to obtain for large areas of land. a, A previous study2 estimating global tree density per hectare relied on data from field plots — samples of these data are shown for western Africa. The inset box in a is in a dry region (with an average annual rainfall of less than 600 millimetres per year), and corresponds to b. Dotted lines indicate the boundaries of average rainfall in millimetres per year. c, d, Brandt et al.1 report the detection of individual tree canopies across western Africa, obtained using an artificial-intelligence approach to analyse high-resolution satellite images. The authors found a higher tree density in dry regions of Africa than did the earlier study. For example, Brandt and colleagues’ analysis of the area corresponding to the inset box in a produced the tree density per hectare shown in c. They identified the size and location of individual tree canopies (green), as shown in d for an area corresponding to the inset box in c. Tree information at this level of detail was not available in the earlier study. (Images made using data from refs 1 and 2.) (Springer Nature remains neutral with regard to jurisdictional claims in published maps.)

There are, of course, caveats and limitations to Brandt and colleagues’ work and the potential for scaling up their approach to a global analysis. Successful canopy detection declined drastically below a canopy diameter of 2 m, owing to constraints imposed by the spatial resolution of the imagery, and consistent with earlier work3. Although we can expect further improvements in the spatial resolution of satellite images, it becomes pertinent to ask what minimum canopy size is needed to characterize woody-plant communities in various regions. For global tree-canopy mapping, if we assume that the computational and storage challenges associated with large data volumes can be overcome, the biggest roadblock would lie in developing efficient approaches for automated classification and delineation of canopies. Brandt and colleagues’ deep-learning method required an input of approximately 90,000 manually digitized training points. This approach becomes untenable for work on a global scale, and more-automated (unsupervised) methods for extracting information from satellite imagery would be necessary4.

A related problem is the ability to distinguish between what might look like one large canopy and adjacent, overlapping canopies of different individual trees. To improve canopy separation, Brandt et al. used a weighting scheme in training their convolutional neural network, but still resorted to a ‘canopy clump’ class to describe aggregated canopy areas of more than 200 m2, suggesting that the separation approach was not always effective. For application in wetter regions, where overlapping canopies in woodlands and forests are common, canopy delineation and separation methods will need refinement and automation to be feasible at global scales.
Yet more challenging is the identification of tree species. Although feasible, on the basis of canopy colour, shape and texture5, it will be particularly tricky at regional and global scales and across biodiverse ecosystems. The mapping of individual tree canopies by species will probably remain at the top of the Earth-observation research community’s wish list for some time6.
In the years ahead, remote sensing will undoubtedly provide unprecedented detail about vegetation structure as data from a range of sources — including light detection and ranging (lidar), radar and high-resolution visible and near-infrared sensors — become more readily available7. Satellite-derived high-resolution data on tree canopy size and density could contribute to the inventory and management of forests and woodland, deforestation monitoring, and assessment of the carbon sequestered in biomass, timber, fuel wood and tree crops. The ability to map the size and location of individual tree canopies using such satellite data will complement information available from other instruments that provide data for tree height, vertical canopy profiles and above-ground wood biomass. Continuing research will be needed to develop more-efficient canopy-classification algorithms. In the meantime, Brandt and colleagues have clearly demonstrated the potential for future global mapping of tree canopies at submetre scales.

doi: 10.1038/d41586-020-02830-3

References

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Brandt, M. et al. Nature https://doi.org/10.1038/s41586-020-2824-5 (2020).

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Draper, F. C. et al. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2020.08.003 (2020).

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National Academies of Sciences, Engineering, and Medicine. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (Natl Academies Press, 2018).

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NEWS AND VIEWS
14 October 2020

Targets for ecosystem restoration are usually specified in terms of the total area to be restored. A global analysis reveals that the benefits and costs of achieving such targets depend greatly on where this restoration occurs.

Simon Ferrier

Simon Ferrier is at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australian Capital Territory 2601, Australia.
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Figure 1 | Tree planting during forest restoration in Madagascar.Credit: RIJASOLO/AFP/Getty

The declaration by the United Nations of 2021–30 as the UN Decade on Ecosystem Restoration is drawing worldwide attention to the challenge of restoring natural ecosystems that have been degraded or converted (for agricultural use, for example)1. Ecosystem-restoration targets already feature prominently in global and national policy frameworks aimed at limiting ongoing biodiversity loss and climate change. These targets are set mainly in terms of the total area or percentage of land to be restored. But how can this restoration effort be best distributed spatially to maximize benefits for both biodiversity conservation and efforts to tackle climate change? Writing in Nature, Strassburg et al.2 address this crucial question across all of Earth’s biomes (broad zones of vegetation adapted to particular climates). To do this, they analyse data on the benefits and costs of restoration, using information assembled at high spatial resolution across the entire global land surface.
Ecosystem-restoration targets have long been regarded as complementing targets for protecting relatively intact ecosystems. For example, the Aichi Biodiversity Targets3 for 2011–20, which were established under a key UN biodiversity treaty, the Convention on Biological Diversity, coupled the ambition of restoring “at least 15 per cent of degraded ecosystems” with that of increasing the coverage of protected areas to include “at least 17 per cent of terrestrial and inland water, and 10 per cent of coastal and marine areas”. However, until now, the science of prioritizing where best to invest in ecosystem restoration at global and national scales has lagged behind the many notable scientific advances made in prioritizing additions to protected areas4.

One of the biggest challenges in prioritizing areas for restoration (Fig. 1) is balancing the benefits for biodiversity conservation against those for climate-change mitigation. Forests are usually the biomes with the highest potential to sequester carbon. However, all biomes, including non-forest biomes such as natural grasslands and shrublands, can contain ecosystems in urgent need of restoration to prevent the extinction of species found only in those ecosystems. Even areas offering similar potential for carbon sequestration within the same biome (for example, in tropical rainforests) can vary greatly in terms of potential restoration benefits for biodiversity conservation. This is because such benefits depend on the number and uniqueness of the species associated with a given area of that biome, and the extent to which these species have lost habitat elsewhere across their range.
Balancing benefits is further complicated by variation in the probable costs of ecosystem restoration in different parts of the world — both the direct costs of restoration and the indirect costs of forgoing income from other land uses, particularly agricultural production. Strassburg and colleagues confront this daunting prioritization challenge head-on using a new multicriteria approach based on a mathematical technique called linear programming. This enabled them to optimize restoration outcomes that balance the benefits for biodiversity and climate-change mitigation, and the associated costs, in a variety of ways. The authors carried out their analysis using state-of-the-art data sets that describe the spatial distribution of: ecosystem types expected in the absence of major human activity; current land uses; the potential for carbon sequestration by living and dead organic matter; habitats of vertebrate species; and expected restoration costs.

Strassburg et al. show that the benefits and costs of restoring a given total area of land depend very much on where this restoration is undertaken. Prioritizing the spatial distribution of restoration using a single criterion of benefit or cost generally performs poorly in achieving desirable outcomes for the other criteria. For example, restoring 15% of the world’s converted lands by focusing solely on maximizing benefits for climate-change mitigation would achieve only 65% of the gains potentially achievable for biodiversity (assessed as the resulting reduction in risk of species extinctions) if the restoration focused instead on maximizing biodiversity benefits. Restoration focused solely on minimizing costs would achieve only 34% of the maximum potential gain for biodiversity and 39% of the potential gain for climate-change mitigation. Encouragingly, however, optimizing for all three criteria simultaneously yields a solution that would achieve 91% and 82% of potential gains for biodiversity and climate-change mitigation, respectively, while maximizing cost-effectiveness.
These findings have major implications for the setting and implementation of global targets for ecosystem restoration. A key discovery by Strassburg and colleagues is that the total area restored is a relatively weak metric of how restoration might help in reaching fundamental goals for biodiversity conservation and climate-change mitigation. This is conveyed most compellingly by the finding that the reduction in risk of species extinctions that is achieved by different spatial allocations of the same total area of restoration can vary by a factor of up to six. Thus, any high-level goal for ecosystem restoration, and associated indicators for assessing progress, should ideally be specified in a way that ensures actions are directed towards areas that will contribute most effectively to achieving fundamental biodiversity and climate goals.

Strassburg and co-workers’ study is particularly laudable for linking perspectives on ecosystem restoration to bridge the domains of biodiversity conservation and climate-change mitigation. However, challenges remain in further linking such prioritization to other key drivers and pressures, and other types of action beyond restoration. Multiple interactions between these factors will together determine overall global outcomes for biodiversity and climate. Consider, for example, the scope of such interactions just in relation to the goal of preventing species extinctions. Strassburg and colleagues’ extinction-risk modelling assumes that the distribution of potentially suitable environments for species will remain fixed, despite growing evidence that many of these distributions are already shifting, or are likely to shift over time, owing to climate change5. Research assessing the combined effects of land use and climate change on biodiversity suggests that not considering climate-change effects might lead to a severe underestimation of extinction risk6.
The authors’ modelling also assumes that all habitat currently provided by intact ecosystems will remain intact. But, given current trends in ecosystem degradation worldwide7, it seems probable that the area of habitat available for species will ultimately be determined not only by gains made through restoration, but also by the interplay of such gains with losses occurring elsewhere in the extent and integrity of ecosystems8. The magnitude and spatial configuration of future losses will, in turn, be determined by ongoing interactions between socio-economic drivers of demand for converted lands, and actions aimed at either reducing the demand itself, or ameliorating the effect of this demand by protecting key areas of intact habitat from conversion9.
The role of such interactions in shaping ultimate outcomes underscores the need to take these interactions into account when defining, implementing and assessing progress in achieving global targets10. The post-2020 global biodiversity framework (see go.nature.com/36fqq44), currently being developed for adoption by the parties to the Convention on Biological Diversity, offers a timely opportunity to address this need by explicitly defining interlinkages between any agreed ecosystem protection and restoration targets and the framework’s over-arching biodiversity goals.

doi: 10.1038/d41586-020-02750-2

References

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Temperton, V. M. et al. Restor. Ecol. 27, 705–719 (2019).

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Strassburg, B. N. et al. Nature https://doi.org/10.1038/s41586-020-2784-9 (2020).

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Convention on Biological Diversity. COP Decision X/2: Strategic Plan for Biodiversity 2011–2020 (2012).

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Dinerstein, E. et al. Sci. Adv. 6, eabb2824 (2020).

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Pecl, G. T. et al. Science 355, eaai9214 (2017).

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Di Marco, M. et al. Glob. Change Biol. 25, 2763–2778 (2019).

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Beyer, H. L., Venter, O., Grantham, H. S. & Watson, J. E. M. Conserv. Lett. 13, e12692 (2020).

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Maron, M. et al. Nature Ecol. Evol. 4, 46–49 (2020).

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Leclère, D. et al. Nature 585, 551–556 (2020).

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Nicholson, E. et al. Trends Ecol. Evol. 34, 57–68 (2019).

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