1.Bayala, J., Sanou, J., Teklehaimanot, Z., Kalinganire, A. & Ouédraogo, S. Parklands for buffering climate risk and sustaining agricultural production in the Sahel of West Africa. Curr. Opin. Environ. Sustain. 6, 28–34 (2014).Article
Google Scholar
2.Stringer, L. C. et al. Challenges and opportunities in linking carbon sequestration, livelihoods and ecosystem service provision in drylands. Environ. Sci. Policy 19–20, 121–135 (2012).Article
Google Scholar
3.Schnell, S., Kleinn, C. & Ståhl, G. Monitoring trees outside forests: a review. Environ. Monit. Assess. 187, 600 (2015).Article
Google Scholar
4.LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).ADS
CAS
Article
Google Scholar
5.Darkoh, M. B. K. The nature, causes and consequences of desertification in the drylands of Africa. Land Degrad. Dev. 9, 1–20 (1998).Article
Google Scholar
6.Ribot, J. C. A history of fear: imagining deforestation in the West African dryland forests. Glob. Ecol. Biogeogr. 8, 291–300 (1999).Article
Google Scholar
7.Fairhead, J. & Leach, M. False forest history, complicit social analysis: rethinking some West African environmental narratives. World Dev. 23, 1023–1035 (1995).Article
Google Scholar
8.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS
CAS
Article
Google Scholar
9.Ickowitz, A., Powell, B., Salim, M. A. & Sunderland, T. C. H. Dietary quality and tree cover in Africa. Glob. Environ. Change 24, 287–294 (2014).Article
Google Scholar
10.Baudron, F., Chavarría, J. Y. D., Remans, R., Yang, K. & Sunderland, T. Indirect contributions of forests to dietary diversity in Southern Ethiopia. Ecol. Soc. 22, 28 (2017).Article
Google Scholar
11.Angelsen, A. et al. Environmental income and rural livelihoods: a global-comparative analysis. World Dev. 64, S12–S28 (2014).Article
PubMed
Google Scholar
12.Reed, J. et al. Trees for life: the ecosystem service contribution of trees to food production and livelihoods in the tropics. For. Policy Econ. 84, 62–71 (2017).Article
Google Scholar
13.Brito, J. C. et al. Unravelling biodiversity, evolution and threats to conservation in the Sahara-Sahel. Biol. Rev. Camb. Philos. Soc. 89, 215–231 (2014).Article
PubMed
Google Scholar
14.Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).Article
PubMed
Google Scholar
15.de Foresta, H. et al. Towards the Assessment of Trees Outside Forests (Resources Assessment Working Paper 183) (FAO, 2013).16.Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).ADS
CAS
Article
PubMed
Google Scholar
17.Axelsson, C. R. & Hanan, N. P. Patterns in woody vegetation structure across African savannas. Biogeosciences 14, 3239–3252 (2017).ADS
Article
Google Scholar
18.Schepaschenko, D. et al. Comment on “The extent of forest in dryland biomes”. Science 358, eaao0166 (2017).Article
PubMed
Google Scholar
19.Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).ADS
CAS
Article
PubMed
Google Scholar
20.Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS
CAS
Article
PubMed
Google Scholar
21.Brandt, M. et al. Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands. Nat. Geosci. 11, 328–333 (2018).ADS
CAS
Article
PubMed
Google Scholar
22.Brandt, M. et al. Woody plant cover estimation in drylands from Earth observation based seasonal metrics. Remote Sens. Environ. 172, 28–38 (2016).ADS
Article
Google Scholar
23.Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).ADS
CAS
Article
PubMed
Google Scholar
24.Ronneberger, O., Fischer P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds. Navab, N. et al.) 234–241, (Springer, 2015).25.Muller-Landau, H. C. et al. Comparing tropical forest tree size distributions with the predictions of metabolic ecology and equilibrium models. Ecol. Lett. 9, 589–602 (2006).Article
Google Scholar
26.Buchhorn, M. et al. Copernicus global land service: land cover 100 m: epoch 2018: Africa demo. https://land.copernicus.eu/global/products/lc (2019).27.Wood, S. A. & Baudron, F. Soil organic matter underlies crop nutritional quality and productivity in smallholder agriculture. Agric. Ecosyst. Environ. 266, 100–108 (2018).CAS
Article
Google Scholar
28.Sandbrook, C., Sunderland, T., & Tu, T. N. in Forests and Food (eds Bhaskar, V. et al.) 73–136 (Open Book, 2015).29.Rasolofoson, R. A., Hanauer, M. M., Pappinen, A., Fisher, B. & Ricketts, T. H. Impacts of forests on children’s diet in rural areas across 27 developing countries. Sci. Adv. 4, eaat2853 (2018).ADS
Article
PubMed
Google Scholar
30.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).ADS
CAS
Article
PubMed
Google Scholar
31.Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).ADS
Article
Google Scholar
32.LeCun, Y. et al. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems 2 (ed. Touretzky, D. S.) 396–404 (Neural Information Processing Systems Foundation, 1990).33.Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (eds Bischof, H. et al.) 3431–3440 (IEEE Computer Society, 2015).34.Sermanet, P. et al. OverFeat: integrated recognition, localization and detection using convolutional networks. Preprint at https://arxiv.org/abs/1312.6229 (2014).35.Cordts, M. et al. The cityscapes dataset for semantic urban scene understanding. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (eds Bajcsy, R. et al.) 3213–3223 (IEEE Computer Society, 2016).36.Simpson, A. L. et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. Preprint at https://arxiv.org/abs/1902.09063 (2019).37.Perslev, M., Dam, E., Pai, A. & Igel, C. One network to segment them all: a general, lightweight system for accurate 3D medical image segmentation. In Medical Image Computing and Computer Assisted Intervention (MICCAI) (eds Shen, D. et al.) 30–38 (Springer, 2019).38.Koch, T., Perslev, M., Igel, C. & Brandt, S. Accurate segmentation of dental panoramic radiographs with U-nets. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI) (eds Davis, L. et al.) 15–19 (IEEE Computer Society, 2019).39.Srivastava, N. et al. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).MathSciNet
MATH
Google Scholar
40.Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning (ICML) (eds Bach, F. & Blei, D.) 448–456 (PMLR, 2015).41.Odena, A., Dumoulin, V. & Olah, C. Deconvolution and checkerboard artifacts. Distill https://distill.pub/2016/deconv-checkerboard/ (2016).42.Sadegh, S., Salehi, M., Erdogmus, D. & Gholipour, A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In International Workshop on Machine Learning in Medical Imaging (eds Wang, Q. et al.) 379–387 (Springer, 2017).43.Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).Article
PubMed
Google Scholar
44.Hengl, T. et al. Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions. PLoS ONE 10, e0125814 (2015).Article
PubMed
Google Scholar
Source: http://feeds.nature.com/~r/nature/rss/current/~3/-1RB6WN-VU4/s41586-020-2824-5