Response to Comment on “The extent of forest in dryland biomes”

Bastin, Jean François; Mollicone, Danilo; Grainger, Alan; Sparrow, Ben; Picard, Nicolas; Lowe, Andrew J.; Castro, R. C.

doi:10.1126/science.aao2079

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The validation data, because it is derived from high‐resolution satellite imagery, potentially could have difficulty separating tree cover from shrub cover (Schepaschenko et al., 2017). However, although it is not error‐free, the Global Drylands Assessment dataset has been shown to be relatively unbiased at a global scale and used conservative criteria (shadows, canopies ≥ 3 m in diameter) to separate tree from shrub cover (Bastin, Berrahmouni, et al, 2017; Bastin, Mollicone, et al, 2017). Furthermore, most of the restoration area highlighted by study 1 has a tree canopy height ≥ 5 m (Figure S1; Los et al., 2012), which meets the 5 m tree height definition used by the AFLRO forest map and the HGFC (Hansen et al., 2013; P. Potapov, personal communication).…”

Section: Causes Of Mis‐estimatesmentioning

confidence: 99%

A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps

Fagan

2020

Global Change Biology

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Two recent global maps of tree restoration potential have identified vast regions where tree cover could be increased, ranging from 0.9 to 2.3 billion hectares. Both maps, however, emphasized dryland regions, with arid biomes making up 36%-42% of potential restoration area. Dryland biomes have repeatedly been recognized as inappropriate regions for expanding tree cover due to the risks of biodiversity loss, water overconsumption, and fire, so maps that highlight these regions for restoration must sustain careful scrutiny. Here, I show that both recent attempts to map restoration potential in arid regions have been hindered by underlying errors in the global tree cover maps they used. Systematic underestimates of existing sparse tree cover led directly to large overestimates of the potential for tree recovery in drylands. The Atlas of Forest Landscape Restoration Opportunities (Laestadius et al., Unasylva, 2011, 62, 47) overestimated tree restoration potential across a third of arid biomes by between 7% and 20% (55-166 million hectares [Mha]). Similarly, Bastin, Finegold, Garcia, Mollicone, et al. (Science, 2019, 365, 76) overestimated tree restoration potential across all arid biomes by 33%-45% (316-440 Mha). These inaccuracies limit the utility of this research for policy decisions in drylands and overstate the potential for tree planting to address climate change. Given this long-standing but underappreciated challenge in mapping global tree cover, I propose various ways forward that keep this lesson in mind. To better monitor and restore tree cover, I call for re-interpretation and correction of existing global maps, and for a new focus on quantifying sparse tree cover in drylands and other systems.

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Section: Causes Of Mis‐estimatesmentioning

confidence: 99%

A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps

Fagan

2020

Global Change Biology

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“…In addition, the comparison between the FAO's GDA method and the best CNN-based model could have involuntary classification errors, due to human failures in the photointerpretation of the plots or to an update of the images in Google Maps (between FAO's assessment and ours) that would show changes in tree cover. Since the accuracy (F1-measure) of FAO's GDA tree cover estimation is relatively low [26][27][28], we should not expect high consensus with any method that has high accuracy. If a high consensus exists, it would imply that the new method is as inaccurate as FAO's GDA [19].…”

Section: Cnns To Estimate Tree Cover In Drylandsmentioning

confidence: 99%

“…FAO's GDA used the augmented visual interpretation method (implemented in the Collect Earth software) on very high resolution images (VHR) from Google Earth TM . However, FAO's GDA was controversial and several studies raised many sources of uncertainty [25][26][27][28][29], mainly related to soil background effects and to the biases and subjectivities introduced by hundreds of operators worldwide. In any case, FAO's GDA required a vast effort, which limits the use of this methodology for monitoring.…”

Section: Introductionmentioning

confidence: 99%

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

et al. 2020

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Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management.

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“…Visual interpretation of very high‐resolution imagery, employed for data validation, confirms good results in deriving statistics on forest cover over large regions (Schepaschenko et al, 2019). As reported in the literature, photointerpretation can lead to an error that is less than 10% in estimating the extent of forest at a global scale (Bastin, Berrahmouni, et al, 2017; Bastin, Mollicone, et al, 2017; Schepaschenko et al, 2017). Using prior maps with satisfactory accuracy, integrating the accuracy of visual interpretation and performance of classification methods could be implemented in order to extend this approach to broader regions.…”

Section: Discussionmentioning

confidence: 97%

Characterizing historical transformation trajectories of the forest landscape in Rome's metropolitan area (Italy) for effective planning of sustainability goals

et al. 2021

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With the aim at developing a landscape dynamics framework for environmental planning and management and testing the effectiveness of protected areas in achieving the 2030 Agenda of the United Nations sustainability goals, we characterized the historical transformation trajectories of forest area changes from 1936 to 2010 in the Metropolitan City of Rome Capital (Italy). Remote sensing‐based products coupled with landscape pattern metrics and fragmentation analysis have been implemented, comparing different historical forest maps. The results show a remarkable forest area gain – from 17.6% to 25.5% – thanks to 68,299 ha of recently established forest. Statistical descriptors showed that the highest relative gain occurred in mountain zones, confirming a wide European forest recovery pattern in marginal areas from past deforestation and overexploitation. Deforestation mainly occurred in the flat and hilly areas where almost 26,000 ha of forests were lost since 1936. In summary, two main forest landscape dynamics were reconstructed: (I) the increase of forest cover fragmentation in the lowland areas; and (II) the rise in the forest area in the interior sectors of the mountain landscape, mainly within protected areas. Restoring the forest ecosystem's bioecological integrity has been highlighted as an urgent action for biodiversity conservation and carbon mitigation. In lowland areas, the study revealed the urgent need to establish new protected areas and rewilding spaces as landscape metrics are relatively below the sustainability targets for healthy forest ecosystems. The proposed framework can be used for testing the effectiveness of environmental planning and management in other forest landscapes to achieve the Agenda 2030 goals.

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Response to Comment on “The extent of forest in dryland biomes”

Cited by 8 publications

References 7 publications

A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps

A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

Characterizing historical transformation trajectories of the forest landscape in Rome's metropolitan area (Italy) for effective planning of sustainability goals

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